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?