As someone who’s been deeply entrenched in the evolution of marketing technology, I’ve witnessed firsthand the dramatic shifts that have defined the B2B landscape.
In the early 2000s, most marketing teams were structured around rudimentary lead generation methods. Websites were just glorified brochures, and the real magic happened over the phone. I was on the front lines answering the 1-800 hotline, fielding inbound leads, and answering product questions.
Deals happened through conference calls and in-person meetings.
Our teams often operated in silos, and measurement was limited to basic metrics like direct response rates, call volumes, hold times, and lead conversion.
Fast forward to the digital age, where the advent of marketing automation platforms, CRM systems, cloud computing, and advanced analytics began to reshape the marketer’s playbook.
Everything went online, and marketing became a much larger playground. Search engine marketing, display ads, review sites, email marketing—we all had to rapidly shift our strategies to keep up.
And the website went from being a nice-to-have digital brochure to a can’t-live-without marketing goldmine of digital footprints.
Suddenly, we could connect dots that had previously been invisible, attributing campaigns to revenue and optimizing the entire customer journey.
Yet, even as we embraced the digital transformation, the human element of marketing remained critical—understanding pain points, crafting compelling stories, and building trust required human nuance.
Now, with agentic marketing stepping onto the stage, we’re witnessing another major paradigm shift. AI agents don’t just amplify efficiency; they open up entirely new possibilities. From hyper-personalized experiences to predictive insights, AI is turning data into actionable strategies at an unprecedented scale unlike anything else I’ve seen in my career.
During my time as CMO at Salesforce, I watched the industry wrestle with adopting the cloud—a technology that fundamentally changed how we do business—and led the charge of bringing our customers into the future.
As AI agents establish roots in our tech stacks, I feel a similar excitement and innovative buzz in the tech sphere as I did then—except it’s on a much faster, much more grand scale.
In this book, we’ll break down the old way of B2B marketing and identify the problems agentic marketing is finally solving. We’ll give you a comprehensive guide to this new era in B2B marketing—as well as case studies from teams who are already thriving with AI agents—and share how we’re cultivating a modern mindset when it comes to marketing.
Agentic marketing puts marketers back in the driver’s seat and gives them total control over their inbound pipeline generation targets, and soon, we know there will be AI agents optimizing every aspect of our jobs.
It all starts with the funnel of the future.
The last 25 years have been a period of extraordinary transformation in the world of software, but marketing and sales existed long before Silicon Valley and SaaS.
From the analog processes of the 1990s (oof!) to the AI-driven solutions of today, marketing leaders have been on a continuous search for efficiency, scalability, and customer-centricity in their marketing efforts.
To understand how we got to the agentic marketing funnel, we need to keep our roots in mind. Prior to the digital transformation, we still referenced a marketing funnel to help clarify who owned which stages of the marketing and sales process.
Marketing owned the top and middle of the funnel. Their role was to attract leads, nurture them, and pass “sales-ready” leads to the sales team. Metrics like marketing qualified leads (MQLs) were the primary measure of success.
Sales owned the bottom of the funnel. They turned MQLs into opportunities and ultimately closed deals. Success was measured in terms of quotas and revenue.
This division worked well in a world where the buyer’s journey was linear, and information flowed from brands to consumers in a relatively controlled and predictable way. As our world went from physical to digital, the traditional marketing funnel’s foundations still held true, and they translated into what we would all recognize today.
But there was one major change as the world transitioned to online-first that created a need for the traditional marketing funnel we all know and love.
The website.
The website is your brand; they’re synonymous. Everything your buyers need to know can live in one place, completely under your control. Marketing’s biggest goal went from getting someone to call your 1-800 number to getting someone to your website.
Suddenly, there was a place to consolidate all of your marketing materials and speak to buyers—a place to drive them to the bottom of every ad and every commercial. The website became the new brochure, and it changed everything about how we interacted with our buyers, blowing up the traditional journey and rendering it completely irrelevant.
The website became the most critical asset for any demand generation team and created a new digital-first norm in sales cycles.
And at first, this was amazing. We tossed forms up on the site to gather any traffic that popped up, which revealed a new problem:
What in the world do we do with all these leads? What does the process look like now that we aren’t exclusively meeting face-to-face or on the phone? Who owns what parts of the sales process? How does marketing make sure they’re sending quality leads, and how do they know that sales is working these leads?
The traditional marketing funnel that arose to meet this new moment looks something like this:
We created a new mid-section in the funnel that accounted for all of these new problems, and a new metric that helped marketing sleep at night:
The marketing qualified lead (MQL)
Using tools to assign points to certain engagements and actions (clicked a link on an email? Point! Registered for a webinar? Three points!), marketers developed lead scoring systems to attempt to identify the prospects that were ready to talk to sales.
Once all the bogus leads were stripped out (no, [email protected] isn’t going to buy software), marketers created a lead database and set the criteria for which leads were sales-ready (i.e., marketing qualified leads) and which fell below that threshold and needed to be nurtured until they showed stronger signals of buying intent.
MQLs were the leads that showed the biggest potential for buying—filling out forms or calling sales hotlines.
Everyone else?
They would get back in the queue for periodic email blasts or retargeting campaigns until they were deemed ready.
These MQLs became a contract between marketing and sales—we give you a high-enough scoring lead, and you go forth and convert them into revenue.
And it almost worked.
Except that another issue emerged:
Who works the leads?
Marketers and sales teams put their heads together, and they came up with sales development representatives, or SDRs. Marketers were too busy driving campaigns to generate interest, and account executives (AEs) were too busy working and closing deals, so a new role was born.
The role of the sales development representative (SDR) emerged as a solution to bridge this gap between marketing and sales.
At first glance, the system seemed efficient: marketers concentrated on driving awareness and interest, while SDRs ensured that sales teams only worked on high-quality opportunities.
However, it didn’t take long for cracks in the foundation of this model to emerge.
While SDR teams were designed to alleviate the workload for AEs and give marketers confidence that all their leads were being properly handled, this trust has always been tenuous at best. Marketers frequently found themselves questioning whether SDRs were truly maximizing the value of their hard-earned leads.
This mistrust stems from several issues we’ve all come up against:
These challenges aren’t a reflection of poor performance—they’re simply the reality of relying solely on humans to drive your funnel.
The triangle of lead generation—marketing, SDRs, and sales—presents a critical flaw: Much of the traffic that marketing generates never gets properly addressed.
At best, these untapped leads are included in generic, automated nurture sequences; at worst, they’re ignored entirely. Consider these staggering results of recent studies, revealing the cracks in the SDR model we’ve cultivated over the last two decades:
An analysis of more than 2,200 American companies found that those who attempted to reach leads within an hour were nearly seven times more likely to have meaningful conversations with decision-makers than those who waited even sixty minutes.
Consider the impact of just one gap in coverage. Stacy State from NextGen Healthcare shared, “We had 1,400 missed chats a year. We’ve gone from 1,400 to zero, resulting in $7.5M in pipeline and over $690K in recurring revenue.”
This significant gap in lead handling represents a massive opportunity cost. Untouched leads either move on to competitors or fail to convert due to a lack of engagement, but SDRs simply aren’t capable of following through with every single lead, every single time.
They have limits, too.
The SDR model was designed to solve one problem, but created several others.
For nearly two decades, the marketing qualified lead (MQL) has been the cornerstone of B2B marketing success metrics, because this number is the only aspect of pipeline goals marketers could control. Marketers have measured their value and progress by how many MQLs they could deliver to sales, and sales has judged marketing on the same number. On the surface, it’s a logical partnership.
But there’s a problem: MQLs are a vanity metric.
They don’t tell the full story of how a lead moves through the funnel, nor do they directly reflect the revenue impact of your marketing efforts. And worse, MQLs often create friction between marketing and sales teams.
The MQL was designed as a truce between sales and marketing. Marketers agreed to provide leads that met specific criteria—such as fitting the ideal customer profile (ICP) or engaging with certain types of content—and sales agreed to work those leads.
But here’s the issue:
MQLs don’t always reflect buying intent
Just because someone downloaded a whitepaper or clicked on an email doesn’t mean they’re ready to engage with sales yet.
They incentivize quantity over quality
Marketers are under pressure to hit their MQL targets, often at the expense of lead quality, which results in wasted time for SDRs and sales reps.
They create silos
Marketing focuses on generating MQLs, while sales teams measure success based on revenue. This misalignment leads to frustration and mistrust between teams.
At their best, MQLs represent an imperfect attempt to gauge interest. At their worst, they’re a distraction from the metrics that actually drive growth.
Even with all of these cracks in the foundation, the traditional marketing funnel was the best we had.
Until now.
Before we deep dive into how agentic AI has overhauled B2B marketing, it’s worth taking a moment to refresh yourself on the last few years, catalog exactly what has changed, and observe how our worlds have been completely turned upside down by AI.
Generative AI did not magically appear in November of 2022, though it may feel like it.
This technology’s journey to the mainstream came from years of development and has several building blocks underpinning its compelling use cases for marketers in particular.
Machine Learning (ML) powers predictive analytics and customer segmentation, allowing marketers to identify patterns and anticipate behavior with speed and accuracy we simply couldn’t match as humans.
Natural Language Processing (NLP) allows AI to understand, generate, and personalize content. It’s also changing how we interact with data by replacing clunky spreadsheets with more natural, human-like dialogue. Now, teams can explore insights and make decisions simply by asking questions, just like they would in a conversation.
Predictive Analytics forecasts buyer behavior to help us better prioritize our efforts when it comes to lead follow-up and conversion.
All of these technologies came together in a powerful package when tools like ChatGPT became widely available to the public, creating a domino effect across software companies.
The questions quickly went from “What can this thing really do?” to “How can this change my business?”
The clear business use cases emerged pretty quickly:
Automation
AI automates tasks that used to take us a lot of manual labor and time to accomplish. Lead scoring, lead follow-up, email outreach—all of these things are now autonomous and free humans up for more nuanced efforts.
Personalization
With so much data available to these models, AI can synthesize all of your disparate systems and form a cohesive image of a buyer instantly, identifying what content they likely need next.
24/7 operation
AI works around the clock, closing coverage gaps and giving your team time back, without sacrificing pipeline.
Revenue impact
By streamlining all of these processes that used to take us forever and optimizing every stage of the funnel, AI directly ties marketing efforts to measurable business outcomes.
Generative AI changed the game for us just a few short years ago—every software company out there instantly had a copilot available to assist their users in moving faster, and now we’re on the precipice of yet another major evolution in these models.
The fully autonomous AI agent.
The next frontier in artificial intelligence, agentic AI, is a model that is able to take initiative, make decisions, and act independently toward a goal. Unlike other AI models, humans aren’t giving explicit instructions via prompts to start these models toward a goal.
Agentic AI leverages advanced algorithms to autonomously adapt based on real-time data.
At its core, agentic AI combines machine learning, predictive analytics, and contextual understanding to operate autonomously and engage users conversationally. These systems don’t have to adhere to predetermined workflows and rules but rather operate within guardrails to achieve goals based on guidelines and real-time data impacting their courses of action.
As Levi Worts, AI Marketing Council Lead at SUSE, puts it, "The agentic marketing funnel allows us new approaches that we previously didn't have the bandwidth to accomplish. The true superpower of AI is that it allows humans to hyperfocus on relationship building. All of the tools we're applying are simply to get us closer to the relationship that we want to build with our customers."
It’s this shift from wide-net casting to laser-focused engagement that agentic AI unlocks at scale.
AI agents are able to take the workflows we create and evolve them, and outperform their human counterparts in speed, accuracy, and scale.
When integrated into B2B marketing tech stacks, AI agents are able to do three things that completely revolutionize marketing efforts.
1. Anticipate needs: By analyzing historical and real-time data, agentic AI predicts what actions will drive the most impact, whether it’s engaging a lead, scheduling a follow-up, or optimizing campaign spend.
2. Act proactively: Instead of waiting for instructions, agentic AI identifies opportunities and takes the necessary steps to capitalize on them, such as nurturing leads with personalized content or reallocating budget to a high-performing ad.
3. Learn continuously: Through feedback loops, agentic AI refines its strategies and improves its performance over time, ensuring that it stays aligned with evolving business goals.
Generative and agentic AI models address all of the limitations of traditional marketing funnels and are redefining how marketing and sales teams approach lead generation and nurturing.
So, how do these AI agents fit into this next evolution of the marketing funnel?
The next era in B2B marketing isn’t just “powered” by AI. We’ve already surpassed the “AI-powered” era in adoption.
The next evolution takes the marketing funnel and completely transforms it into a fully autonomous motion.
As the marketing landscape grows increasingly complex, the need for tools that drive efficiency and precision has never been greater. It can feel like this revolution is moving at lightning speed (frankly, because it is!), and staying focused on how your buyers benefit helps keep your head in the game when it comes to rethinking your funnel.
AI models, including generative and agentic AI, are no longer optional for modern marketers. They are essential for staying competitive in today’s digital-first environment.
Here’s why:
Today’s customers demand seamless, personalized experiences across every interaction. AI enables marketers to meet these expectations by delivering the right message to the right person at the right time, which creates a more engaging and satisfying customer journey.
With budgets often stretched thin, AI helps marketers do more with less. By automating repetitive tasks and optimizing campaign performance, AI allows teams to focus on strategic initiatives that drive revenue.
Companies that adopt AI gain a significant advantage over those that rely solely on traditional methods. AI’s ability to analyze vast amounts of data and act on insights in real-time ensures that marketers can stay ahead of trends and outpace competitors.
AI bridges the gap between marketing and sales by providing actionable insights that both teams can use to achieve shared goals. This alignment not only improves efficiency but also enhances overall business performance.
The rapid pace of technological advancement means that businesses must continuously adapt to stay relevant. By embracing AI, marketers position themselves to evolve alongside industry changes, ensuring long-term success.
In an era defined by data-driven decision-making, AI is not just a tool—it’s a strategic imperative. Modern marketers who embrace these models will be better equipped to navigate challenges, capitalize on opportunities, and deliver measurable impact at every stage of the funnel.
Generative and agentic AI have not only addressed the limitations of the traditional funnel but have also redefined how marketers and sales teams approach lead generation and nurturing.
As we delve deeper into the agentic marketing era, we’ll also explore how to implement these game-changing technologies and how to expand your mindset from AI-powered marketing to autonomous marketing.
For a few years now, SaaS leaders have sidestepped the implications of AI for roles like the SDR, but it’s becoming increasingly clear that some of these roles are prime candidates for automation.
The agentic marketing funnel will be commonplace in a matter of months, not years, and this kind of rapid evolution requires a certain mindset to keep up.
Many companies are already feeling this shift.
It’s that realization—missed opportunities hiding in plain sight—that’s fueling the need for a new way of thinking.
So, how can you, as a marketing leader, get your head around all of this change and make strides toward adopting these powerful tools without getting bogged down?
To thrive in this era, marketers must cultivate a mindset that not only embraces AI but also leverages its capabilities to innovate and achieve measurable outcomes. This is not just about adopting new tools; it’s about fundamentally rethinking how strategies are developed, executed, and refined.
There are three key driving factors motivating this need for future-forward marketers to embrace AI:
AI technologies are evolving at an unprecedented pace, and with them come new possibilities for creating personalized, data-driven marketing strategies. Staying ahead means adapting to these changes and proactively seeking ways to integrate AI into existing workflows.
Today’s consumers expect more than one-size-fits-all messaging. They demand hyper-relevant interactions tailored to their needs and preferences. AI provides the tools to deliver this level of personalization at scale and transforms how brands build relationships with their audiences.
Early adopters of AI are already reaping significant benefits, from increased efficiency to higher conversion rates. For marketers, embracing AI is not just a choice—it’s a necessity to remain competitive in a landscape where agility and innovation are key differentiators.
Rethinking your strategies to be AI-first can feel like a total overhaul of everything you know, but it’s actually your blueprint for every strategy moving forward.
Define clear objectives—AI without intent hurts more than it helps.
AI should serve a specific purpose within your marketing strategy. Whether it’s improving lead qualification, optimizing ad spend, or enhancing customer retention, define what success looks like and set measurable goals. AI applied to the right use cases is a power-up, but when you’re using it in the wrong lanes, it will always fail.
Flashy AI features that aren’t necessary will degrade your team’s adoption and trust in these tools over time, so be thoughtful in your integration strategies.
Spend time evaluating.
Not all AI tools are created equal, and selecting the right ones is critical.
Consider:
Make it work for your stack—not the other way around.
When you’re investing time and money into new AI tools, you’ll have much more success if you're already set up to maximize new tech, rather than having to burn it all down and rebuild from the ground up.
Baby steps! First, evaluate:
Measure and optimize.
AI provides a wealth of data to evaluate performance, and as with any marketing initiative, vibes alone aren’t enough to prove your case.
Use insights from AI to:
Adopting AI isn’t just about technology; it’s about transforming how marketers think and operate. To step boldly into the AI era, consider these guiding principles:
The agentic marketing mindset is about more than adopting new technologies; it’s about embracing a transformative approach to strategy and execution.
By cultivating curiosity, leveraging data, fostering collaboration, and building confidence in AI, marketers can unlock new opportunities and drive meaningful impact. The future of marketing is here, and it’s powered by AI.
Those who embrace it with boldness and creativity will lead the charge into a new era of innovation and success.
The new B2B marketing funnel isn’t simply AI-powered.
It’s fully autonomous.
The agentic marketing funnel harnesses the power of agentic AI in the form of AI SDR agents, eliminating wasted time, missed opportunities, and dropped leads.
The traditional marketing funnel is a human-centric system designed around capacity, not opportunity. Where marketers were boxed in before, hoping that their MQLs wouldn’t get lost in the shuffle, they’re now unleashed to focus on bigger, more meaningful activities that drive eyes to the website, confident that every single lead will be worked.
The inability of a human SDR to quickly service MQLs is why marketers hesitate to commit to a pipeline number. They can confidently deliver a steady stream of MQLs, but what happens next is out of their hands. They can’t control how quickly an SDR engages with those leads or scale that function.
It’s a common turning point: recognizing that human teams, no matter how talented, simply can’t scale fast enough to keep up with growing demand.
For a long time, this process was the best we had, but it was never efficient. The entire funnel was designed around human capacity. In 2025, we’re no longer optimizing for human limitations.
The agentic marketing funnel removes human capacity from the equation, replacing it with AI SDR agents that can handle every lead interaction autonomously.
You’ve likely seen a hot take or two on social media about AI SDR agents.
They get a bad rep, but we believe that comes from the bevy of early-stage startups attempting to automate processes that aren’t as ripe for automation as inbound lead conversion is.
In the wrong use case, with the wrong expectations, of course AI will fail. It’s no different from human employees in that way. You wouldn’t automate your account executives’ processes, for example. Those conversations are often highly nuanced and emotionally driven in nature, two things AI isn’t as suited for.
But in the right context, with proper training and guardrails, AI SDR agents truly shine.
So, what is AI great at?
And what role in the sales process is highly repetitive, requires a ton of manual follow-up, data analysis, and multi-tasking?
The SDR.
There are two types of AI SDR agents:
Inbound SDR agents handle leads who are already showing interest. They identify and engage your website visitors, nurture leads, and keep moving them down the sales funnel.
Key features: Inbound AI SDR agents are key to ensuring ICP visitors convert. These agents proactively greet and engage buyers, answer questions instantly, and dynamically adapt to your buyers’ needs in order to book a meeting. If they don't book, AI SDR agents can follow up autonomously via email and keep the lead warm until they're ready to meet with your human reps.
Benefits: The main benefit of an inbound AI SDR agent is that it achieves higher conversion rates, thanks to timely and personalized follow-ups. They help maintain engagement with prospects who have already expressed interest and ensure that no opportunity slips through the cracks.
Outbound sales, on the other hand, reach out to prospects who may not be familiar with your brand. Outbound AI SDR agents work to generate interest and initiate conversations with cold leads.
Key features: Outbound AI SDR agents handle cold email campaigns, generate personalized messages, and intelligently manage follow-up sequences to engage prospects. They use data like company size, industry, and the prospect's role to tailor messages, making outreach feel more personal, even when it's automated.
Benefits: The biggest benefit of an outbound AI SDR agent is increased reach. By automating cold outreach, outbound AI SDRs can engage far more prospects than a human SDR could, making lead generation much more scalable and cost-effective.
While the future of B2B sales will likely combine automation in both functions, inbound AI SDR agents are powering the agentic marketing funnel, scaling marketing efforts, and making the inbound motion more cost-efficient and lightning-fast.
These powerhouse models aren’t just ChatGPT in a mask. They can take on the full scope of a human SDR, from engaging buyers in real-time to nurturing them with personalized follow-ups.
Inbound AI SDR agents revolutionize the funnel in three key ways:
AI SDR agents can instantly assemble a full profile of every lead that visits your website with the kind of accuracy human SDRs could never accomplish. They can access all of your CRM data and data platforms to form a coherent picture of your lead and marry it with engagement data as they interact with content on your website.
AI SDR agents can autonomously engage your inbound website traffic in real time. They can surface relevant offers, answer questions, and book sales meetings with the right members on your team. They’re always on message, and your buyers never have to wait to talk to sales.
If buyers don’t convert on your website, AI SDR agents will follow up with contextual, personalized emails. Buyers who fill out forms, download content, or register for an event will also receive timely email campaigns designed to drive conversion without any humans having to work these leads.
Inbound AI SDR agents help scale inbound pipeline generation motion, giving your buyers the information they need faster, so they can book a meeting with your human reps and move into a sales cycle quickly and efficiently, all without adding to your headcount.
These agents are working across your most important marketing channels: the website and the inbox. This orchestration ensures a smooth handoff from email click-through to onsite conversion with a consistent and instant experience provided by all-knowing AI SDR agents.
Ultimately, the agentic marketing funnel optimizes your entire inbound pipeline generation motion from lead capture, to nurture, to meeting booking, so your human team can focus on bigger and better things.
And you, as a marketing leader, can trust that there’s no pipeline left behind.
Before, marketers really only had control over the MQL, and we held tight to that metric to prove that our work paid off.
But in the agentic marketing funnel, the MQL is no longer a significant metric to track.
Now that AI SDR agents have the capacity to scale and engage with all leads, not just some, we can pivot to what truly matters:
Instead of tossing MQLs over the fence and hoping human SDRs work them, marketers now have AI SDR agents working for them, giving them control of every aspect of the lead qualifying and converting process, delivering qualified pipeline to sales.
Another massive impact for marketers?
Those leads that your arbitrary algorithm scored as either “not the right fit” or “not engaged enough” were really missed opportunities that now get worked by AI SDR agents, creating additional pipeline you didn’t even know you lost out on.
Marketing leader Kristina Finseth realized after adding an AI SDR agent to her team that over 60% of her MQLs weren’t getting worked by the sales team because they weren’t considered top-tier accounts. After implementing an AI SDR agent, they saw a 130% increase in meetings booked.
The agentic marketing funnel allows teams to find pipeline they didn’t realize was left on the table, engage and convert all traffic on their website simultaneously, and do it at scale, 24/7.
It’s the dream for both marketing and sales leaders.
The transition to an AI marketing funnel can feel overwhelming.
After all, automating your inbound pipeline and replacing manual processes with AI SDR agents represents a significant shift in how teams operate.
There are two limiting factors that most teams feel unsure about when it comes to entering this new era of AI agents:
It can feel scary in a time when pipeline targets are high, and the pressure is on to take a big swing with new technology.
Rethinking your team structure to account for role automation can feel intimidating.
Here’s the good news: On both fronts, you don’t have to make the leap all at once. In this chapter, we’ll cover phased approaches for both areas of change and real results we’ve seen for customers who have made the shift.
By adopting a crawl, walk, run approach to the agentic marketing funnel, you can gradually implement AI SDR agents, allowing your team to get comfortable with the technology, optimize processes, and scale at your own pace.
This method has already helped companies like Demandbase, Greenhouse, and Cin7 achieve transformative results while minimizing disruption.
Here’s how you can follow the same phased approach to automate your funnel and drive measurable pipeline growth.
Buying behavior doesn’t stop at 5 PM, especially for modern buyers.
The easiest way to introduce AI SDR agents into your funnel is by addressing a common pain point we all fear: missed opportunities during off-hours.
The easiest way to introduce AI SDR agents into your funnel is by addressing a common pain point we all fear: missed opportunities during off-hours.
The team at FreshBooks noted, “Prospects might be looking for accounting software at 10 PM, and we weren’t able to help them the same way we could in the middle of the day. That’s where an AI SDR agent has been really helpful.”
Traditional human SDRs typically work 9-to-5, leaving nights, weekends, and holidays uncovered. This creates gaps in your lead engagement process, with high-intent prospects who are often left waiting hours—or even days—for a response.
Get started
Case study
Bloomreach was already streamlining their inbound pipeline generation with an automated meeting booker experience, but they took it to the next level when they hired an AI SDR agent to capture leads, engage them, and book meetings in a more conversational manner. This led to a 1.4x increase in meetings booked year over year.
Once your team is comfortable with AI SDR agents handling off-hours leads, it’s time to expand their role.
In the “walk” phase, you can allow AI SDR agents to handle a larger share of your inbound traffic, including lower-priority leads during regular business hours.
This phase is ideal for businesses where human SDRs are overwhelmed by the volume of inbound leads or are focusing exclusively on high-priority prospects.
How to expand
Case study
Crunchbase modernized their inbound pipeline generation with an AI SDR agent and saw instant value, but they still weren't taking full advantage of its agentic capabilities. When they expanded to allow that AI SDR agent to nurture inbound leads via email, they saw a 3x increase in meetings booked.
The final phase is where the real transformation happens.
In the “run” stage, your AI SDR agent takes over the entire inbound pipeline funnel, engaging, qualifying, and nurturing every lead that comes through your funnel. Human SDRs only step in when a buyer is ready for direct interaction.
How to fully automate
Case study
Quantum Metric sunsetted their inbound SDR program and hired an AI SDR agent to work all of their inbound leads. They found that visitors who engaged with their AI SDR agent were 8x more likely to book a meeting.
Adopting AI SDR agents is a big change, but the crawl-walk-run approach ensures your team can adjust gradually while seeing measurable results at every stage. This phased rollout minimizes risk, builds confidence in the technology, and allows you to optimize processes before scaling.
The agentic marketing funnel isn’t just a vision for the future, but rather it’s a proven strategy that businesses are using today to unlock new growth opportunities.
But how do these changes impact your internal team structure?
Role automation can be a hot topic, understandably, but new technology that evolves roles as we know them is perhaps the only thing that hasn’t changed over the last few decades.
Twenty-five years ago, marketers relied on print ads and trade shows as their only option to build brand momentum, requiring a host of roles that faded away as the digital age took hold. Despite this, the workers didn’t disappear. They evolved their skill sets to keep up with modern demands and stay competitive.
Print artists became graphic designers.
Ad copywriters became content marketers.
In the same way, the SDR role will fade to automation, and new entry-level roles in tech sales will emerge. The World Economic Forum predicts that AI will lead to the creation of 97 million new jobs in 2025.
The fact is, labor is always the biggest business expense, typically accounting for over 50% of a company’s total budget. Before AI agents, the software market fueled the labor market because you needed humans to operate any new tools you purchased.
Now, with agentic AI, labor comes built into the software, creating an overlap in the software and labor marketers we’ve never seen before. This requires a more in-depth examination of our team structures and preparation for new roles to rise.
The teams that thrive in this new era of work will be the ones who thoughtfully and strategically automate roles that are already on their way out and pave new paths forward for bigger, more engaging human roles.
A typical sales org today relies on SDRs, but tomorrow, they won’t.
So, how can you prepare your team for these shifting roles?
Think of this phased approach with a today, tomorrow, and next year timeline.
Today
Now is the time to start evaluating AI SDR agents and how they fit into your sales org. Your human SDRs are already hearing about role replacement; those conversations should begin now. What is your company’s POV on why this is the right move, and what’s your plan for the next year? What technology are you evaluating to bring on as a partner in this transformation? Refer to the AI SDR Agent Hiring Guide at the end of this book for a handy resource on evaluating AI SDR technology.
Tomorrow
SDR teams become hybrid, featuring an AI SDR to help alleviate pressures on resource-constrained sales teams and fill any gaps. Your human SDRs should start working toward their next move now—are they on track to move into an account executive role? Maybe it’s time to carve out some accounts for them to manage. Are their skill sets better suited for outbound prospecting? Refocus their time now that there are fewer coverage needs, thanks to your AI SDR. Maybe they have an interest in marketing? Now is the time to get them involved in ongoing marketing initiatives and work on a path for their future.
Next Year
Today’s SDR managers will be tomorrow’s AI SDR agent experts, managing the onboarding, training, and maintenance of AI SDR agents’ knowledge bases and engagements to ensure they’re performing at their absolute best.
There will be roles available in the coming years that we aren’t even conceptualizing right now. The best way to manage this revolution within your organization is to embrace the shift and stay ahead of it now, so you aren’t rushing to catch up later.
Securing budget for new initiatives in GTM functions can be a complex endeavor, especially when we’re still in the early days of AI SDR agents.
The challenge begins with the fundamental differences in how various executives view and manage budgets. Sellers and Chief Revenue Officers (CROs) primarily think about sales capacity, often using headcount-based, ratio-driven models like 12 sellers to 6 AEs, to 4 SDRs, and 2 managers.
CMOs, on the other hand, typically budget in buckets such as people, programs, and infrastructure, aiming for percentage splits like 40/40/20 or 45/45/10 across these categories.
CFOs view budgets through expense categories like operating expenses and capital expenditures. That said, headcount remains a critical focus both to prevent “empire building” and to track gross productivity metrics like ARR per employee.
This difference in perspective is often where the struggle to secure budget begins.
Introducing AI SDR agents complicates budgeting because it disrupts established ways of thinking and historical comparisons.
A significant shift occurs as AI SDR agents substitute people expenses for infrastructure expenses, reducing headcount costs while increasing software/infrastructure costs.
This can break historical ratios and trends, which CFOs closely monitor.
For instance, if you hold your SDR team at four instead of growing to eight, you save approximately $400K in fully loaded headcount costs. This $400K represents a potential investment ceiling for an AI SDR agent.
However, investing heavily in infrastructure might lead to accusations of being "toy happy" or questions about why funds aren't being spent on demand-generating programs.
Securing budget for an AI SDR agent is challenging precisely because it "upsets the budgeting apple cart" and requires aligning these different executive perspectives.
To navigate this, Dave Kellogg suggests learning to play the budgeting game.
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While ROI is ultimately important—Dave Kellogg would even say it’s king—he advises not starting with a detailed ROI model in budget discussions.
A detailed ROI model can be built by finance professionals, accounting for cost savings and software costs, but the preferred strategy is to start by presenting the impact on the overall plan, which is what Kellogg calls The Punchline Approach.
The Punchline Approach is a clear commitment to delivering improved business results. For example, if last year's demand gen budget (inclusive of SDRs) of $1.6 million generated 400 opportunities at a cost per opportunity of $4,000 and a pipe to spend ratio of 12.5, the punchline for the coming year with an AI SDR agent investment would be a commitment to generate more opportunities (e.g., 600) with a higher budget (e.g., $2.25 million) but at a more efficient cost per opportunity (e.g., $3,750) and a better pipe to spend ratio (e.g., 16).
Dave Kellogg emphasizes the power of this approach: “The punchline is: ‘If you let me spend this money, here's what I'm going to do for you next year—and it's going to be more efficient than we were last year.’”
He explains why he prefers this over just the ROI model: "Because I think you're kind of giving the audience what they want to hear... When you're talking in a budget review meeting to the CFO and CEO, they just want the punchline."
The ROI model should still be built and kept ready, but it's the impact on key business metrics (like cost per opportunity and pipe to spend) that resonates at the executive level.
If the Punchline Approach succeeds and executives are interested (“Hey, wait a minute—we can generate more opportunities at $3.75K instead of $4K? Huh, I’m interested in that.”), they immediately switch into risk mode.
This is a critical phase, similar to a homebuyer getting nervous about potential problems after deciding they want the house. The questions will shift to: "How do you know this is going to work?" and "How are you sure we should do it?”
To address this, you need to demonstrate thorough diligence:
Customer references
Talking to other customers who are using the AI SDR agent for similar purposes, in the same industry, and at a comparable scale. These references help confirm the vendor has solved this problem before.
Vendor reputation
Evaluating the vendor's credibility, pedigree, investors, and technology. This addresses whether they are a reliable partner.
Project plan
AI works around the clock, closing coverage gaps and giving your team time back, without sacrificing pipeline.
The goal is to reassure conservative stakeholders, particularly CFOs, by proving you've done your homework beyond just running the ROI numbers.
You want to demonstrate that you are "in the middle of the fairway" for the vendor's core strategy.
In other words, you're not asking them to take on anything risky or experimental; you're aligned with what they already do best.
Dave Kellogg advises, "You want to come back and say ‘There's no better answer—we are in the middle of the fairway for this vendor's strategy. They do this all day long.’”
As part of the risk discussion and planning, consider deployment models.
While some companies are aggressive, going "both feet in", classic risk mitigation strategies like regional or vertical experiments exist but are less commonly seen.
A more popular approach is the inbound/outbound split, often moving human SDRs to outbound and giving inbound leads to the AI SDR agent. The strategic goal here is to push higher-value work to humans and lower-value work to AI.
Dave Kellogg suggests a twist on this: sending high-value inbound (like ABM or strategic target accounts with good titles) directly to sales reps, bypassing both human and AI SDR agents "basically high value inbound so either ABM accounts account based marketing accounts and or strategic target accounts right with good titles so like directors and VPs at target 200 accounts all go directly to the sales rep as they should anyway". Everything else would go to the AI SDR agent, which acts like an AI-driven nurture for non-strategic accounts or contacts. This model balances value and efficiency.
Finally, don't overlook any top-down AI budget that might already be available due to board or CEO pressure to adopt AI tools—this is often the easiest source of funds if it exists.
Otherwise, securing budget requires a strategic approach combining the Punchline Approach, the readiness of a detailed ROI model, and a robust plan to address perceived risks.
The agentic marketing era is here, and teams seeking sustainable growth face a transformation in how they generate pipeline.
AI SDR agents are leading this shift, but the market is flooded at the moment, and it can be hard to know what tools are worth the time and investment and what aren’t.
There are two distinct paths to deploying these agents:
Before choosing, you'll need clarity on three pivotal questions:
The most basic setup can automate reminders or qualifiers, but if you're aiming to own pipeline at scale, your agent must be able to:
Key Insight: Identify must-have capabilities (e.g., omnichannel support, real-time personalization, and deal handoff) and choose a solution that delivers the full package. Anything less will limit ROI.
Building requires engineers, integrations, UI/UX, and continuous iteration. It can be a long, resource-intensive journey. In contrast, buying offers:
Key Insight: Weigh time-to-value against resource availability. Teams in growth mode shouldn’t be tied down building custom pipelines while growth stalls.
AI SDR agents are not "set it and forget it." You need mechanisms for:
Ready-made solutions often come with dashboards, support, and innovation baked in. DIY setups can become burdensome without dedicated expertise.
Here’s a side-by-side breakdown of 14 critical decision factors to help your team decide between building with an agent builder platform or buying a solutionized AI SDR agent.
Decision factor | Build with an agent builder platform |  Buy a solutionized AI SDR Agent |
|---|---|---|
Out-of-the-box functionality | Nothing is pre-built: Any basic AI SDR agent templates aren’t production-ready and require significant dev time just to reach baseline functionality. | Turnkey solution: Comes fully configured with enterprise-grade functionality and best-practice guidelines, tailored to your business from day one. |
Data connectivity | DIY integrations: You must manually integrate your business knowledge, GTM data, website data, and buyer intent data. | Powerful native integrations: Real-time, unified view of leads and accounts from deep integrations across your GTM stack and the website. |
AI decisioning (aka the agent brain) | Building the brain from scratch: You must manually engineer how the agent interprets and takes action on your data— constructing AI logic from the ground up. | Purpose-built brain for pipeline: AI decisioning is built in with pipe gen best practices. The agent dynamically and intelligently adapts engagement to each lead and self-optimizes. |
Visibility into agent strategy | Black box: Your team has to piece together what the agent is thinking and doing behind the scenes, leaving them in the dark regarding context. | Full transparency: Provides a clear view into agent's bespoke activity and game plan for each lead and account, providing context like a true teammate. |
Omnichannel conversion | Multi-channel = multiple agents: You will have to build and stitch together separate agents with separate logic for each channel, like website and email. | One unified agent across channels: One agent works your website and email using the same brain—creating a cohesive, seamless buyer experience. |
Customizable Salesforce integration | Limited integration: Requires heavy customization for the agent to work beyond a limited set of Salesforce records. | Deep integration: Hooks directly into accounts, visitors, and custom objects and is ready to mirror your GTM motion out of the box. |
Developer resourcing | Expertise and heavy lift required: Need in-house developers with flow, APEX, and prompting skills or costly third-party services to build and maintain. | Heavy lifting done for You: Implementation, maintenance, and support are fully managed—no internal developers or third-party services required. |
Business prioritization | Pulls focus from your product: Even if you have the talent in-house, it will divert focus from your core priorities and product. | Your product remains #1 priority: Internal teams stay focused on core business priorities instead of building, fine-tuning, and maintaining your agent. |
Total cost of ownership | Costs add up fast: Beyond licensing, expect headcount costs, build delay costs, maintenance costs, and consulting costs to pile up over time. | Clear, all-in pricing: One predictable price includes the agent, the platform, implementation, and ongoing support for the entirety of the partnership. |
Pricing model | Unpredictable credit-based pricing: The more the agent engages, the more you pay, making pricing a black box that is misaligned with your goals. | Scalable, plan-based pricing: Aligns with your success—you’re encouraged to engage more, convert more, and scale without fear of surprise overages. |
Opportunity costs | Slow time to value: Months of planning, development, and testing to push out a first version delays pipeline, revenue, and efficiency gains. | Fast time to value: Launch in weeks and start seeing pipeline and impact quickly. Rapid speed to value more than justifies the upfront investment. |
Reliability | Support falls on you: Uptime, availability, and QA fall entirely on your team, and you get limited access to log data. Any slip-ups break trust with buyers and internal teams. | Dedicated support, 24/7/365: Your external-facing agent is continuously monitored and optimized by a dedicated team to ensure it's always on and always at peak performance. |
Ease of use | IT owns agent updates: No user-friendly UI means developers are needed for even basic changes and gate progress. Marketers lose control over a tool that impacts their pipeline. | Marketers own agent updates: Easy to prompt, update, and optimize without developer intervention. The agent moves at the speed of your fast-paced campaigns. |
Future innovation | Struggles to keep pace: Without dedicated AI expertise in-house, your agent will fall behind in a rapidly advancing market. | Remains cutting-edge: Comes with a team focused on shipping constant improvements and innovation so your agent stays ahead of the curve. |
Mural struggled with the question of building vs. buying. While building was conceivable, they realized it wasn't the best use of their resources. Instead, they chose a vendor-built AI SDR agent, Piper, for its deep CRM integration, reliable infrastructure, and focus on performance, allowing them to skip the rebuild and focus on insights and pipeline.
Begin with pilot projects that demonstrate AI’s value. The crawl, walk, run strategy outlined in the previous chapter for implementing AI SDRs holds true for any AI technology. You don’t have to do it all at once, and there’s value in taking smaller steps while still keeping a big vision in mind.
Assess your team: do you have the engineering, data science, integration, and AI skills needed to build and support a full solution?
Short-term traction + limited budget? Buy.
Large internal AI team + specific custom needs? Build.
The chosen solution must support continuous monitoring, refinement, and adaptability as both the business and AI landscape change.
By the end of this era, teams equipped with the right evaluation framework—like the one above—will be able to:
The future belongs to those who can orchestrate intelligent, autonomous agents—not only to execute but also to evolve with intent and insight.
Hey, we’re Qualified, and we’re changing how companies generate pipeline.
We’re leading marketing teams into the future of pipeline generation with one powerful agentic marketing platform and Piper the #1 AI SDR agent.
We're ushering in an entirely new way to generate inbound pipeline autonomously and efficiently, putting marketers back in the driver’s seat.
To learn more about Piper the AI SDR Agent, visit qualified.com/piper and see the transformational power of the agentic marketing funnel for yourself.
See how Piper handles your outreach firsthand! Challenge her with tough questions and objections, just like your pickiest prospects would.
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