In 2006, we founded Marketo and I helped create the traditional B2B marketing playbook—the one built on MQLs, marketing automation, and measuring everything. For years, it worked brilliantly. Marketing finally had a seat at the revenue table, with clear metrics and predictable processes.

Now we're watching that playbook fall apart, just as AI is fundamentally changing how we work and how we buy.

It's the perfect storm: declining effectiveness of traditional demand generation colliding with transformative new technology.

And those aren't just tactical shifts or new tools to master: The very nature of what it means to be a marketer—from how we engage buyers to how we structure our teams to what we measure—is changing.

There's a lot to unpack, so let's dive into my predictions for how B2B will evolve in 2025 and beyond!

Grading my 2024 predictions

A prognosticator is only as good as his or her accuracy, so before we dive into 2025 let's first briefly evaluate how well I did with my 2024 predictions.

AI in B2B marketing is overhyped in the short run, and underestimated in the long run. Grade: A

The market was flooded with shallow AI applications in 2024—mostly basic GPT wrappers producing mediocre content and surface-level personalization, leading to a predictable backlash against AI sales development reps (SDRs), automated LinkedIn comments, and so on.

Follow-on prediction for 2025: More companies will rethink how B2B go-to-market can work in the age of AI, and new AI-native startups will emerge that reimagine old ways of doing things. They will begin to show the true potential of AI in GTM. Plus, we've only just begun to understand how AI agents and teammates will change the way we work (see below) —let alone what happens if/when we achieve artificial general intelligence, or AGI (which I don't think will happen in 2025).

Email marketing isn't dead or dying—but it is evolving (quality over quantity). Grade: B-

Email isn't going anywhere. Professionals check their email 15+ times daily, on average. And there is still no better alternative as a direct communication channel, especially because you control it (unlike social algorithms). That said, I haven't seen broad evidence that marketers have embraced quality over quantity. If anything, sales reps and marketers are using AI to send even higher volumes of "personalized" emails, many of them cringe-worthy.

Follow-on prediction for 2025: The surge in AI-generated emails will create unprecedented inbox noise, causing users to turn to their own AI tools to tame the onslaught. I hope that companies will stand out by sending fewer, more meaningful messages that respect the intimate nature of the inbox, treating their email lists as valuable owned media rather than mass blast channels—but I won't count on it.

The traditional B2B demand gen playbook will continue to decline in effectiveness. Grade: A

Yes, the traditional playbook continues to struggle. A Pipeline360 survey found about half of B2B marketers will be unable to reach their pipeline growth goals for 2024, while only 37% of RevOps leaders are confident they'll hit 2024 targets. Yet as I also predicted, "only a fraction of companies will fully embrace the new B2B playbook," with too many investors, CEOs, and CFOs still thinking of marketing just as a driver of campaigns and MQLs.

Follow-on prediction for 2025: The traditional MQL-driven, email-blasting playbook will continue to be disrupted, resulting in declining effectiveness... and spurring innovators to find AI-driven opportunities to do better (see my 2025 predictions below).

We will see more content based on original research and data. Grade: B

As audiences seek authentic, trustworthy voices amid the AI content flood, data-driven original research continues to stand out. Peter Walker (102,000 LinkedIn followers) at Carta Data Labs is a great example of someone who does original research well. And yet, surprisingly, it still isn't a common strategy across companies, nor did most CMOs budget for it in 2024.

Follow-on prediction for 2025: I hope to see more companies budget for content based on original research in 2025 (and see prediction 10 below).

Leading companies will build brand by investing in owned media and engaged communities. Grade: C+

Owned media (e.g., websites, email, events) continues to be core to many companies' strategy—and there seems to be an ever-growing number of podcasts as well. Yet relatively few B2B companies have fully embraced publishing content so good that people will pay for it—and then giving it away for free under a subscription model. Hiding content behind opt-in forms is still all too common. So yes, while my prediction that "leading companies" will embrace owned media and communities remains true, I can't give myself full credit.

Follow-on prediction for 2025: This trend will continue: more subscriptions instead of opt-in forms, more owned media strategies, and I hope more "merch" (not swag!) from B2B brands.

New 'XLG' go-to-market motions will continue even as companies embrace blended approaches. Grade: B

Since no major new go-to-market motions emerged, I can't give myself an A. Yet companies certainly doubled-down on blended approaches. Product-led growth (PLG) with a sales overlay has become standard practice (see Zoom, Slack, Figma), and Gartner projects PLG will be part of 90% of GTM strategies by 2025.

Follow-on prediction for 2025: The shift to self-service buying will continue (Forrester predicts over half of $1M+ B2B deals in 2025 will go through self-serve channels). And blended approaches will continue to rule as companies pair GTM motions that naturally reinforce each other—like using outbound to reach enterprises while letting PLG drive SMB growth, or combining ecosystem partnerships with inbound content to maximize distribution. Furthermore, budgets will remain flat after inflation in 2025, making co-marketing with ecosystem partners even more attractive.

B2B marketers will not be prepared for the end of third-party cookies. Grade: A-

Google did NOT phase out third-party cookies in 2024, and instead moved toward offering cookies by choice as well as adding additional IP protection in Incognito mode. However, my core prediction about B2B marketers' preparedness holds true. Change is coming, and current alternatives still do not adequately address B2B needs, particularly for ABM use cases, such as identifying anonymous Web visitors at the account level.

Follow-on prediction for 2025: B2B-specific solutions will emerge to partly fill the cookie void. Watch for growth in cooperative data approaches, such as 5x5 Data Co-Op, where companies pool first-party data to enhance account identification. But privacy regulations will limit such solutions, both in and especially outside the US.

Innovative marketers will start tracking qualified buying groups (QBGs) instead of MQLs. Grade: A-

There certainly was a lot of discussion in 2024 about the traditional MQL model's becoming obsolete (yes, it's time to "bury the lead"). And also about the importance of Buying Groups: Google Trends indicates a 31% increase in search volume for "buying group" between 2021 and 2024. So my prediction that companies will start using QBGs in 2024 seems accurate, even though the way I worded it as only "innovative marketers" was a bit of a gimme (thus the knock down to A-).

Follow-on prediction for 2025: As vendors—including Demandbase, Leandata, 6sense, and others—create content and release functionality to support buying groups, expect to hear even more about the idea in 2025. As Forrester wrote: "Solutions for mapping individuals to opportunities have reached technological reliability and maturity. While organizations may make different choices on the solution types that work best for them, having limited visibility into buying-group behavior is no longer an option for B2B organizations."

Jon Miller's Predictions for B2B Go-to-Market in 2025

Looking ahead to 2025, I see two forces reshaping B2B go-to-market: the death of the traditional marketing playbook (covered in my first three predictions) and the rise of AI agents (explored in eight predictions about how they'll transform the way we work, sell, and engage with buyers):

1. Companies will slowly break from their 'gumball machine' MQL addiction
2. CMOs will work to reframe Marketing's role in revenue
3. Marketers will rebalance budgets toward brand
4. AI agents will gain early traction in the enterprise
5. MOps teams will use AI to trade tactical tasks for strategic impact
6. AI will start to replace junior sales roles but augment strategic sellers
7. Companies will adopt AI SDR agents—but automated cold prospecting will fall flat
8. Seat-based pricing will give way to value-based models
9. Agents will begin to transform how we buy—and how we go-to-market
10. Experiences, relationships, and original content will win as AI filters out traditional marketing
11. Marketing automation platforms will be reimagined for the AI era
    
    

1. Companies will slowly break from their 'gumball machine' MQL addiction

The metrics-obsessed, MQL-chasing B2B playbook I helped develop at Marketo taught executives to view marketing as a gumball machine—in goes budget, out comes leads. But that approach no longer works. When we bombard prospects with unwanted emails just because they downloaded an e-book, we're not building relationships—we're burning bridges. Years of overly promotional content, aggressive meeting requests, and relentless pressure to squeeze more leads from shrinking budgets haven't just failed to deliver results... they've actively damaged our ability to engage buyers meaningfully.

The results speak for themselves: rising customer acquisition costs, declining SDR productivity, and pipeline that's harder than ever to generate.

That's why an increasing number of companies are breaking free from the gumball machine mentality. They are shifting their focus away from generating MQLs and toward doing right by the customer; from short-term metrics to long-term relationships.

This means:

• Stop viewing buyers as MQLs to be harvested, and start seeing them as humans seeking solutions.
• Focus on existing customers rather than constantly chasing net-new ones.
• Create content so valuable people would pay for it—then give it away free.
• Let buyers control their journey instead of forcing them through our funnels.
• Build genuine connections through active listening and community-building.
• Measure success through engagement quality and customer advocacy, not just lead quantity.

Or, as Forrester writes in Budget Planning Guide 2025: B2B Marketing Executives, "B2B marketing leaders must sponsor a shift from revenue obsession to customer obsession."

These aren't revolutionary ideas. They're timeless principles we've buried under automation workflows and pipeline metrics, and it's time to change.

But let's be realistic. Changing deeply entrenched thinking takes time. Most companies won't fully abandon MQLs in 2025. The pressure to deliver immediate pipeline remains intense, and many boards still expect concrete ROI from every marketing dollar (even if delivering those "analytics" relies on flawed assumptions about how buying works).

The full transformation from MQLs to meaningful connections will take at least a decade as companies gradually shift their thinking. The pioneers who start now will build the kind of deep customer relationships that drive sustainable growth. The rest will keep chasing MQLs until they realize the gumball machine is empty.

2. CMOs will work to reframe Marketing's role in revenue

The gumball machine mentality comes from a well-intentioned desire to tie marketing to revenue. That trend is inevitable; Marketing Operations is already moving toward a unified RevOps function, and more marketing teams are reporting to chief revenue officers. Pipeline metrics dominate board discussions about marketing.

Yet, simultaneously, as we've discussed, the gumball machine mentality is ironically leading to declining customer trust, rising acquisition costs, and difficulty maintaining sustainable growth.

Something has to change.

That's why in 2025 more marketing leaders will be attempting to reframe Marketing's value and role within the organization, moving the perception from tactical execution to strategic voice of the market and the customer.

It's not about abandoning revenue responsibility—quite the opposite. It's about recognizing that sustainable revenue comes from deeply understanding the market, building authentic customer relationships, and creating a compelling brand that customers know, understand, and prefer.

As Drew Neisser writes: "If your stated expertise is demand generation, then your domain may shrink. It's not that demand gen will be less important to the organization. It's just that you'll be perceived as a tactician or worse, 'just the marketer.' Great CMOs...demonstrate that the true power of marketing goes well beyond demand."

A related prediction: more marketing leaders will embrace the title "chief market officer" to signal this strategic shift. As Christine Heckart explains, every other C-suite role owns a domain, not an activity (we don't have chief selling officers). A chief market officer gets at marketing's real value: owning deep market expertise, from positioning to customer insights to brand, reputation, and momentum.

But the title isn't a requirement; it matters less than having the strategic discussion about marketing's fundamental value.

Ultimately, the question for marketing leaders isn't whether to be revenue-focused or market-focused. It's how to show that real revenue leadership comes from true market leadership.

3. Marketers will rebalance budgets toward brand

One of the most visible signs of a longer-term, more strategic role for marketing will be a bigger investment in brand-building.

In their 2017 book Media in Focus, Les Binet and Peter Field argued that companies should allocate 60% of marketing budget to brand and 40% to "sales activation." Their 2019 research with LinkedIn updated that ratio for B2B: 46% to brand building and 54% to demand generation.

Yet most B2B companies invest less than that on brand. Insight Partners reported that its portfolio companies with new logo deal sizes <$75K invested only 14% of marketing resources on brand—and just 22% for companies with larger deal sizes. The past few years of expensive capital and tight budgets pushed many companies to cut back even more on harder-to-measure brand investments.

Now the pendulum must swing back. The modern B2B buying journey demands it. We've come to accept that only ~5% of buyers are actively in-market. The other 95% aren't looking to buy—yet. But they will. And when they do, their awareness and perception of your company's brand determines whether you make the shortlist and win.

Want proof? 6sense found that 80% of B2B buyers pick their preferred vendor before contacting Sales. TrustRadius's research reinforces that finding: 78% of buyers chose products they knew before starting research; the proportion climbs to 86% for enterprise buyers.

A few caveats:

• First, like the move away from gumball machine MQL thinking, fully rebalancing our investment in brand will take years, and in 2025 only innovators and agile startups will fully adopt the approach.
• Second, when I talk about brand, I'm primarily NOT talking about spending money. Brand lives in the emotional and rational associations buyers hold about you—ideally captured in just a few words. Achieving that state starts with the fundamentals: a deep customer understanding translated into clear positioning and compelling messaging.
• Finally, investing in brand also doesn't always need to mean slashing your demand gen budget. Executive events, original research, customer advocacy, community-building, authentic thought leadership, and analyst and influencer relations all drive both brand and demand results. I predict the term "brand-to-demand" will become more common. In fact, the Institute of Practitioners in Advertising found that combined brand-and-demand campaigns perform 6x better than demand-only efforts. Demand marketing's impact may fade quickly, but brand investment compounds over time.
  
  

4. AI agents will gain early traction in the enterprise

An "AI agent" is a computer program that combines an AI model (such as an LLM) with additional capabilities to access information, make decisions, and take actions, often to complete tasks without needing a human to watch over it or tell it what to do.

Compare that to an "AI application" such as ChatGPT. An AI application combines an AI model with an interface and other features to enable practical uses such as summarizing content. To use Chris Penn's analogy: like a regular car, it won't do anything until a human asks it to; in contrast, an AI agent is more like a self-driving car, but one usually built with a very specific, narrow purpose in mind.

For example:

• AI application: Writing an email using ChatGPT requires you to provide a prompt, tweak the draft, and manually send it.
• AI agent: A sales outreach agent pulls CRM data, crafts a personalized email based on the client profile, sends it, and follows up automatically—only notifying you if a response needs manual intervention.

AI agents thrive on repetitive tasks that follow a clear process but still deliver a lot of value, without needing constant human oversight: updating CRM scores, running basic email sequences, monitoring support tickets, generating routine reports. In general, if you use it repeatedly as a prompt today, you can likely automate it as an agent tomorrow.

The potential of AI agents hasn't gone unnoticed. Organizations currently allocate about 70% of their budgets to people and less than 10% to software. As agents take on more work, Mayfield estimates, the AI Teammates economy could grow to $6 trillion by 2030, far surpassing today's $660 billion enterprise software market.

Vendors are responding. In 2024, Salesforce introduced Agentforce, its suite of autonomous AI agents for tasks across Sales, Marketing, Service, and Commerce. HubSpot launched four Breeze Agents for content creation, social media management, prospecting, and customer support. Not to be outdone, Google, Microsoft, Oracle, SAP, and IBM have also entered the fray with agentic announcements targeting enterprise use cases. Meanwhile, startups bring agility, focus, and the ability to attract top AI talent, enabling them to compete with established players. The AI Agent Directory lists 462 agents as of November 2024.

Given all this innovation, in 2025 the adoption of agents in the enterprise will move beyond proofs-of-concept—at least for some use cases. In fact, Deloitte predicts that 25% of enterprises using GenAI are expected to deploy AI agents in 2025 (growing to 50% by 2027), and Forrester writes that "AI coworkers will emerge as valued team members in two out of five organizations [in 2025]."

Beyond 2025, agents will reshape not only productivity but eventually also the very structure of work itself. Jeremiah Owyang predicts that within the next 2-3 years AI agents will outnumber humans on the Internet, collaborating with each other and farming out subtasks to other agents—and to humans. This means in the future there will be entire agent-run organizations that serve as your customers, competitors, and even employers. You will have AI employees and coworkers. (You'll need to learn how to be a good manager and colleague to AI teammates.) And it's not far-fetched to imagine you might even one day find yourself working for an agentic boss.

5. MOps teams will use AI to trade tactical tasks for strategic impact

Let's move from the broad enterprise landscape to marketing specifically. I think one of the first places AI agents will gain real traction is in Marketing Operations (MOps).

Today's MOps teams are drowning in digital minutiae. Tactical tasks consume half their day, while they face mounting pressure to handle more complex, integrated campaigns with fewer resources. More martech isn't the answer; adding yet another tool to the stack just creates more complexity to manage.

In contrast, an AI teammate is exactly what MOps professionals need to escape the technology trenches and focus on strategic innovation for the business. The model is elegantly simple: Humans focus on strategy and innovation while AI handles execution and optimization.

In 2025, early adopters will begin to use MOps AI agents for various use cases, such as automated...

• Audience-building and segmentation
• Data-cleansing and enrichment
• Campaign- and program-building based on a prompt or brief
• Creation of multiple versions of campaigns for different segments or for testing
• Analysis of campaigns, accounts, and contacts

Adopting these agents will require MOps teams to evolve. They'll spend less time building campaigns and wrestling with martech tools, and more time on strategic oversight and managing AI performance. Instead of building campaigns, updating salesforce records, or managing event logistics, they'll focus on defining strategy, establishing business rules, managing data infrastructure, and training AI teammates to execute effectively.

I predict that rather than eliminating MOps jobs, these agents will elevate the operations function into strategic architects and business advisers who help the entire revenue team achieve their KPIs.

Two related predictions:

1. When martech is operated primarily by AI agents rather than humans, integration capabilities and APIs will become more crucial than user interfaces and dashboards. That shift might even lead to a "headless martech" movement, with tools that exist solely to support AI agents and which never have human users.
2. MOps will spend more time ensuring data quality and governance to help the AI agents work effectively. You wouldn't throw a new employee into the deep end with messy, incomplete information and expect good results, and the same goes for AI. In 2025, the difference between successful and struggling AI implementations won't be the sophistication of the AI tools—it will be the quality of the data foundation that MOps teams build beneath them.
   
   

6. AI will start to replace junior sales roles but augment strategic sellers

Now let's focus on sales. As Jacco van der Kooij argues, our current GTM inefficiencies stem from reliance on human sellers—expensive to hire, slow to train, and hard to scale. As he says, we can't achieve exponential growth through linear systems... but AI agents represent a system-based approach that can scale automatically, and exponentially.

That's why AI in 2025 will increasingly replace entry-level sales roles by performing tasks such as lead qualification, prospect nurturing, and meeting scheduling (see next prediction). These AI systems always have up-to-date and accurate information (unlike many of today's poorly enabled junior reps); they operate around the clock without breaks; and they respond to inquiries within seconds—delivering a significantly better buyer experience. And they do it at a fraction of the cost: AI can process 1,000 leads per hour, compared with 10 for an SDR team, with minimal human oversight (a 1:100 ratio).

In 2025, AI in sales will mostly involve email automation at early adopter organizations. There are already AI SDR agents focused on email, including Alice from 11x AI, Piper from Qualified, and Ava by Artisan AI. Clay has risen to a $500M+ valuation providing tools to automate data enrichment and prospecting when used in conjunction with tools like SmartLead or Instantly.

Innovators will go even further. Puppydog.io (note: I'm on the board) provides AI-generated personalized demos, and 1mind provides AI agents that can join meetings, give presentations, and answer questions autonomously.

But it's not all AI all the time. Although AI excels at low-risk, repetitive tasks, humans will remain critical as complexity and the cost of mistakes increase. Consider enterprise deals that require nuanced negotiation with other humans, or strategic partnerships where relationship-building and long-term trust determine success. In those scenarios, AI will be used to enhance humans, not replace them.

Here, AI will increase rep efficiency and effectiveness. Today's sellers spend less than 30% of their time actually selling; the rest is consumed by what Bill Binch calls "Sales Pre-Work"—that mountain of research, preparation, and system updates that eat up productive hours.

I predict that in 2025 roughly 25% of companies will deploy AI solutions to prepare briefings before calls, track and log activities, draft follow-up notes, etc.

In the future, AI agents will handle more and more of the sales cycle, including qualification, discovery, education, and simple negotiation, perhaps interacting with buyer agents. And as agents handle everything that can be automated, human sellers will focus more on the complex, high-value interactions that truly need the human touch.

The combined effect? A better buying experience where prospects can progress through the funnel at their own pace, supported by always-on AI assistance, while human sellers focus on complex deal strategy and relationship-building.

Some related predictions:

1. Emotional intelligence, relationship-building, and complex problem-solving will matter more for successful reps than process management or product knowledge. Enable your teams accordingly.
2. How will we train and develop the next generation of sales talent if junior roles disappear? Companies will need to rethink how they identify and develop new talent, including structured apprenticeship programs and longer training periods.
3. We will see the rise of what Brendan Short calls the GTM engineer. This role blends technical skills, like automation and data integration, with a deep understanding of sales and growth tactics. So instead of hiring 10 SDRs, early adopters will hire one or two GTM engineers to manage and orchestrate the "team" of AI agents.
   
   

7. Companies will adopt AI SDR agents—but automated cold prospecting will fall flat

As AI SDR agents gain traction, three distinct use cases are emerging: inbound response, warm outbound engagement, and cold prospecting. Although AI excels at the first two, especially over email, the third will remain problematic, I predict. Here's what's happening and why it matters.

Inbound: When prospects actively express interest through demo requests or meeting signups, speed matters more than nuance. AI agents excel here, providing instant responses, handling initial qualification, and managing the logistics of moving opportunities forward. As a result, human sellers are freed up to focus on strategic conversations that truly need a personal touch.

Warm outbound: This is all about timing and relevance—engaging prospects who've already shown interest through content interactions, event attendance, or intent signals. AI can excel here, too, suggesting next best actions based on prospect behavior and being available to answer questions. Remember, most of these buyers will still not be in-market. It's less about introducing your brand or asking for a meeting, and more about nurturing existing awareness with relevant value.

Cold prospecting: This is where things get messy. Effective cold outreach requires genuine personalization and real business insight—not just mentioning someone's alma mater or latest LinkedIn post. We're all seeing the rise of inbox spam generated by AI prospecting. The challenges with AI automation include the following:

• Potential opt-outs and brand damage from tone-deaf, unmoderated outreach
• Irrelevant or outdated messaging because of poor data quality
• Struggles with conversational nuance and contextual understanding
• Compliance risks with regulations, such as TCPA and privacy laws
• Amplified bad practices when scaled automation lacks proper oversight

So, I predict the following will happen in 2025:

• AI agents will take over most inbound and some warm outbound SDR functions at the innovators and early adopters. Also, AI agents will begin to automatically respond to requests from other AI agents used by buyers (see below). In 2026, it will cross the chasm to the pragmatists.
• Startups and other companies with a low-ACV, high-TAM will experiment with using AI to scale cold prospecting. They'll register some early success, but many will quickly burn through their lists and suffer from a backlash against the spray-and-pray approach.
• Companies with more complex solutions will use AI agents to augment their sellers' prospecting efforts, at a minimum having humans review the AI-generated emails before they go out to important contacts and key accounts.

Related predictions:

1. AI SDRs will blur the traditional handoff between marketing and SDRs. Generating MQLs to toss over the wall to SDRs makes little sense when AI agents can intelligently nurture relationships with every person and account. This shift will accelerate the evolution of metrics away from MQLs toward more meaningful indicators, like engagement quality and true hand-raisers (whether human or AI).
2. It doesn't make sense to use one tool for your marketing emails and another for automated inbound and yet another for warm outbound emails or prospecting. Even emails like event invitations often are more effective if they come from a "human" inbox rather than a marketing HTML email. Over time, we'll see the lines blur between traditional marketing automation and these capabilities.
3. As that happens, the very way we think about marketing departments may evolve. Perhaps we will see teams focused on creating deep customer understanding and building brand and awareness; another on creating content and experiences to nurture known relationships (most not yet in market); and another that manages the AI agents that orchestrate all prospect and customer communications.
   
   

8. Seat-based pricing will give way to value-based models

As AI agents change how enterprise software delivers value, pricing models must evolve to keep up.

Seat-based pricing—the foundation of enterprise software for decades—makes little sense when AI agents replace traditional users and perform the work of multiple employees. Instead, pricing will shift away from charging for access and toward consumption- and outcome-based models, aligning costs more closely with value creation.

Forrester predicts that by 2025, 10% of enterprise software will adopt true consumption-based pricing, signaling a clear move away from static user counts. Salesforce's usage-based pricing for Einstein ($2 per conversation) highlights this shift. These models also address customer preference for pricing that correlates to usage and outcomes rather than access alone.

Enterprise procurement teams traditionally hate unpredictable costs, but they hate paying for unused software even more. They will demand pricing that balances flexibility with predictability. Hybrid approaches—such as usage-based subscriptions with annual consumption credits—will likely dominate.

This transition won't be straightforward. Although seat-based pricing is simple and predictable, usage-based models are more dynamic, tying revenue to adoption patterns and real-world results. Go-to-market teams will need to rethink their approaches. Instead of marketing features, we'll need to articulate value in terms of work completed and outcomes achieved. ROI calculations will shift from theoretical estimates to actual usage and impact. And compensation models for sales teams will need adjustment, perhaps with new approaches that estimate future revenue based on expected usage or outcomes, and other methods that enable commissions to be paid up front while aligning incentives with long-term customer success.

9. Agents will begin to transform how we buy—and how we go-to-market

AI agents won't just change how we work—they'll also transform how we buy.

Soon, instead of manually researching solutions, evaluating vendors, and conducting initial negotiations, human buyers will rely on AI agents to do the heavy lifting.

That is the natural evolution of how AI already filters and synthesizes the ways we navigate information and engage with content. Gmail routes promotional messages to separate tabs and can summarize our emails if we want. Google AI Overviews, Perplexity, and ChatGPT searches provide answers directly in search results without requiring clicks to websites (what's known as "zero-click" search). AI buying agents represent the next step, moving beyond filtering and summarizing to actively guiding purchase decisions.

On the B2C side, agents will guide a shopper to the perfect pair of shoes and alert them to the best deals on their favorite products. In the enterprise, AI agents will act as the "0th member" of buying committees, screening vendors, comparing features and pricing, and ensuring compliance requirements are met. Imagine a CTO instructing their agent to find a new security solution. Instead of combing through websites or downloading whitepapers, the agent analyzes hundreds of offerings, interacts with vendor selling agents, parses documentation, and presents a shortlist of options ranked by the company's specific needs.

That shift is an evolution of the self-service trends already reshaping B2B buying. Buyers have long preferred researching solutions independently, and AI agents will take things to the next level. Forrester reports that buyers already engage in more self-directed interactions than human ones, and many are now comfortable making high-value purchases entirely through digital channels. AI agents will amplify this tendency, acting as buyers' always-on assistants, filtering information and driving decisions autonomously.

For marketers, such a transformation presents profound challenges and opportunities.

Marketing strategies have traditionally assumed that humans consume the content—but that assumption won't hold as AI agents increasingly handle tasks. Content will need to be restructured for both human and AI consumption. Clear, machine-readable data feeds will complement visually engaging websites. Pricing APIs will become as essential as "contact sales" forms. Standardized product specs, optimized for AI systems, will matter more than glossy brochures. Just as SEO expertise became critical for optimizing human search, we'll see the rise of AI Agent Optimization experts to ensure information is accessible, accurate, and relevant for autonomous systems.

The metrics for success will also shift. Instead of Marketing-qualified leads (MQLs), we will measure qualified agent interactions (QAIs)—i.e., how effectively agents parse our information, request additional data, and recommend our product.

Advertising will change too. Free AI buying agents will include paid ads, similar to Google AdWords (Perplexity is already testing ads as "sponsored follow-up questions"). Premium paid agents will filter out anything extraneous—including traditional ads. Specific offers, such as discounts, may get considered and delivered, but branding efforts will increasingly focus less on digital and more on human-centric channels, such as events, experiential campaigns, and TV.

Vendors will also deploy their own AI sales agents to interact with buyer-side agents, both in agent marketplaces and directly with each other. These agent-to-agent conversations will manage the early stages of the buying process—screening vendors, exchanging requirements, and negotiating terms. Human sales teams will still play a vital role, but their focus will shift to managing complex deals and relationship-building, areas where emotional intelligence and strategic thinking remain essential.

2025 will be the year B2B companies start seriously preparing for this shift in buying. The smart ones will get ahead of it.

10. Experiences, relationships, and original content will win as AI filters out traditional marketing

When buyers have access to AI-powered agents that summarize and disintermediate our marketing, what truly stands out? Human experiences, authentic relationships, and original content.

Those three elements share a crucial characteristic: AI can't summarize them.

As Chris Penn aptly notes, there's no value in summarizing an experience—experiences are about emotion, whereas summaries are about information. You can't summarize the energy of a live event, the insights from a peer roundtable, or the connections made at an executive dinner in an AI summary. It's like seeing a photo of a vacation versus actually taking one; the summary might convey information, but it misses the emotion and memory entirely.

In 2025, companies wil increase investment in memorable marketing experiences: intimate C-suite dinners, hands-on product workshops, peer advisory councils, and immersive brand experiences. Although B2C brands pioneered this approach (72% of millennials spend more on experiences than material goods), B2B is catching up.

Rather than running another webinar with a talking head for 45 minutes, companies will add interactive breakout sessions where attendees can network and share insights. Tradeshow booths will evolve beyond product demos to create memorable moments—think collaborative art installations, virtual reality experiences, or even themed photo opportunities that attendees actually want to share. Even virtual events will transform to have parallel online communities in which attendees can network before, during, and after.

The goal isn't just to inform, but to create something worth remembering and talking about.

Relationships and trust form the second pillar. As Justin Gray says, "Relationships are putting the R back in ROI." Whereas information is abundant but unreliable, trusted insights are in short supply and in high demand. No AI summary can replace a trusted peer's saying "this solution worked for us" or a strategic partner's vouching for your capabilities.

What's old is new again. While technology evolves, human nature remains constant: We trust recommendations from people we know and respect. For B2B, three strategies stand out for building those relationships at scale:

1. Strategic partnerships that extend reach and credibility
2. Influential voices who shape industry conversations
3. Vibrant communities where buyers connect and learn from peers

That would explain the surge in ecosystem-led growth (ELG), where companies leverage partner networks to build credibility and reach. Sometimes called "nearbound," ELG helps companies access warm introductions, create credible referrals, and build joint solutions that provide more value to customers. Pavilion found that 60% of SaaS leaders increased their ELG focus in 2024, and Crossbeam reported 50% growth in partner data-sharing, highlighting the importance of partner ecosystem investments to drive scale in a tight-budget environment.

Similarly, the source of information will matter as much as (or more than) the substance, since people will pick their trusted sources and "whitelist" them. (That process has already been underway with regard to the press.) That's why B2B influencer marketing will increase in 2025 and beyond. Though B2C brands pioneered influencer strategies, B2B is catching up—which is why founders and executives are building their influence on LinkedIn, and why companies are developing formal influencer marketing programs.

The trend will only accelerate as Millennial and Generation Z buyers join buying committees. Forrester's Buyers' Journey Survey found that younger buyers rely heavily on external sources, including online communities, to inform their decisions. Social media already ranks among their top three preferred interaction types; and according to Her Campus Media, TikTok is now the primary search engine for over half of Gen Z.

Finally, there's original content. As I discussed in my 2024 predictions, LLMs only remix existing information—they can't create something truly new. That makes original research, proprietary data insights, and authentic thought leadership more valuable than ever. Even when AI summarizes such content, it will cite the source, driving visibility and authority.

Practical guides and detailed strategies will also retain their value. When people search for implementation guidance, they don't want a summary—they want comprehensive instructions and real-world examples. Humor, too, stands out precisely because it loses its spark in summary form.

The implications for B2B marketers: invest in what AI can't replicate. Build communities, not just content libraries. Create experiences, not just emails. Focus on original insights, not derivative thought leadership. Most important, stay human. The real risk isn't AI that filters our marketing—it's forgetting how to connect authentically with our audiences.

11. Marketing automation platforms will be reimagined for the AI era

Today's marketing automation platforms (MAPs) were built in the late 2000s to support the old, failing B2B playbook—and they're as outdated as the playbook they were designed to execute. They're struggling to adapt to the opportunities and challenges created by AI, much less deliver the experiences modern buyers expect.

The limitations of traditional MAPs have become painfully clear. They excel at sending email but stumble with everything else. They're rule-bound, channel-constrained, and perpetually behind the curve of how B2B buying actually works today.

Consider these key shortcomings:

• Rigid rules create generic experiences. Traditional MAPs force us into inflexible workflows that treat every buyer the same. In an era where B2B buyers expect the same personalization they get as consumers, we're still sending one-size-fits-all messages that buyers increasingly ignore or resent.
• Email-centric systems create fragmented journeys. While buyers move seamlessly across channels, our marketing automation remains stubbornly email-focused. The result is a disconnected experience as buyers bounce between generic emails, untargeted ads, and uncoordinated sales outreach.
• Complicated interfaces create growing bottlenecks. When every campaign change requires a technical specialist, marketing teams can't move at the speed of opportunity. That friction renders marketing teams unable to execute their ideas and MOps teams overwhelmed with tactical requests, and buyers therefore receive outdated or irrelevant messages.
• The cost of maintaining these outdated systems continues to rise. Traditional MAP vendors raise prices each year even as innovation has stagnated. Yet most companies continue paying because there hasn't been a viable alternative—creating a vicious cycle of rising costs and declining results.

It's no wonder that 87% of companies are reassessing their current marketing automation platforms, according to Research In Action's latest research report.

But as I discussed in these predictions, marketing is changing dramatically because of AI and AI agents, and the legacy MAPs aren't keeping up. Simply bolting ChatGPT onto your email editor doesn't make your marketing automation AI-ready, any more than adding a spoiler to a Model T makes it a self-driving Tesla. We need new, AI-native platforms.

That's why in 2025 new entrants in the B2B marketing automation space will reimagine the entire category from the ground up for the age of AI. These platforms won't just automate tasks—they'll transform how marketing works. (Hint: stay tuned for what I'm working on next!)

The solutions will provide:

• Native support for accounts, contacts, and buying groups in one unified platform
• Seamless orchestration across email, advertising, sales engagement, website, and more
• Integration with Cloud data warehouses, product analytics, and third-party data

But it's what's under the hood that will dramatically change things: a modern architecture that's AI-native. These solutions will enable marketing where you can simply articulate your strategy—your ideal customers, your key segments, your goals—and AI orchestrates everything else. Where campaigns build and optimize themselves. Where marketing is unchained from being a tactical MQL-creating function and can be a strategic driver of company growth.

These platforms will finally deliver on the promise of automation in marketing automation—the dream of campaigns that actually run and optimize themselves.

Conclusion: AI will ultimately make marketing more human

Something remarkable is happening in B2B go-to-market. AI is dramatically changing how we work, how we buy, and how we will engage with customers and prospects—at the same time as we're seeing the traditional demand generation playbook falter.

The rise of AI and the emergence of a new B2B playbook aren't separate trends; they're two sides of the same coin.

And that is what makes me so excited about marketing in 2025 and beyond. As AI agents take over the mechanical aspects of marketing—the campaign-building, audience segmentation, and performance optimization that consume so much time today—we will be freed to focus on the strategic work of true market leadership.

Put another way, if AI can help tackle the "-ing" in marketing, then we'll be able to focus more on the "market."

While AI handles tactical execution, we'll invest in the initiatives that often sit neglected: developing profound market insights, crafting differentiated positioning, building engaged communities, and creating the kind of experiences and content that defy AI summarization. Allocating more budget to brand-building suddenly becomes more achievable when AI streamlines and optimizes demand generation.

And that's why none of this is about replacing humans with AI. It's about using AI to enhance our uniquely human capabilities.

When everyone has access to the same AI tools, success comes from how strategically you deploy them—and, more important, how you supplement them with human creativity, emotional intelligence, market understanding, and true expertise.

It's ironic: In our rush to embrace artificial intelligence, we're discovering that human expertise, creativity, and brand authenticity are more crucial than ever. Who would have predicted that?