82%
AI Is Now Embedded in Core Workflows
With 43% moderately embedded and 39% deeply embedded, more than four in five organizations have moved beyond experimentation and are using AI across sales and marketing operations.
G2 Research · May 2026
Where AI Stops and Human Selling Wins
How sales and marketing leaders are adopting AI across their workflows, revealing patterns in maturity, automation boundaries, and human-AI collaboration.
82%
AI Is Now Embedded in Core Workflows
98%
Execution Is Where AI Proves Its Worth
85%
Teams Protect Human-Led Relationship Selling
48%
GTM Is Shifting for AI-Mediated Discovery
Executive Summary
What We Found
AI in sales and marketing has moved past experimentation into operating infrastructure: 43% of organizations are moderately embedded and 39% are already deeply embedded across workflows. But this is not turning into a story of full automation. In response to deeper adoption, teams are drawing a clear augmentation boundary, with 85% automating top-of-funnel and administrative work while protecting human-led relationship selling.
This division of labor is where value is concentrating. AI is proving itself in execution, with 98% reporting benefits in sales execution and 79% identifying relationship-building and trust as the core human advantage. As a result, the winning model is blended rather than autonomous: AI scales research, drafting, and process speed, while people carry trust and differentiation. That internal maturity is now extending outward, with 48% already adapting GTM and content for AI-informed buyers and AI-mediated discovery.
98%
Execution Is Where AI Proves Its Worth
Nearly all respondents report value from AI in sales execution, especially through faster research, drafting, preparation, and workflow efficiency.
85%
Teams Protect Human-Led Relationship Selling
Most organizations automate top-of-funnel and administrative work while keeping trust-based selling and buyer relationships firmly in human hands.
48%
GTM Is Shifting for AI-Mediated Discovery
Almost half of teams are already optimizing content and buyer journeys for AI-informed buyers, signaling that internal AI maturity is now shaping external market strategy.
Why this matters · For SaaS vendors
Why SaaS Vendors Should Care About This Study
Buyers are not asking whether AI belongs in sales and marketing — they have already answered. 82% of organizations operate with AI moderately or deeply embedded across workflows, and 48% are actively rebuilding GTM for AI-mediated discovery. Vendors who keep selling AI as a feature add-on are talking to a market that has moved on. The findings below identify exactly where the willingness to pay, the gaps in execution, and the differentiation opportunities now sit.
OPPORTUNITY 01
Reprice and Reposition Around Workflow Orchestration
43% of organizations are moderately embedded and another 39% are deeply embedded with AI. Only 18% are still in early-stage use. The market is past pilots, so vendors should tier pricing and packaging by depth of embedding, not seat count alone, and sell measurable orchestration outcomes to mid-stage adopters.
Vendor Implication
Move from "AI feature" messaging to workflow transformation offers that bundle integration, governance, and enablement.
OPPORTUNITY 02
Build for Human-In-The-Loop, Not Autonomy
85% of respondents automate top-of-funnel and admin while keeping relationship selling human-led. Only 2% support broad end-to-end automation. Selling "autonomous AI sellers" into this market will hit a ceiling fast.
Vendor Implication
Position products as rep amplifiers with mandatory human review at handoffs — prospecting, scheduling, drafting, and CRM updates first.
OPPORTUNITY 03
Lead With Execution Outcomes, Not Generic Productivity
98% of respondents say AI delivers value in sales execution, but only 41% see both efficiency and better messaging or conversion; 47% see efficiency alone, and 10% still report limited or unproven impact. The proof gap is real.
Vendor Implication
Differentiate with conversion-grade instrumentation — engagement, reply rates, meetings booked, close rates — instead of vague time-saved claims.
OPPORTUNITY 04
Strengthen the Human Side of the Stack
79% say human-led relationship-building and trust are the key human advantage; another 15% emphasize judgment and soft skills. Buyers will not pay for tools that erode trust at the moment of purchase.
Vendor Implication
Bundle enablement, coaching, and call-intelligence features that elevate seller judgment rather than replace it.
OPPORTUNITY 05
Win the AI-Mediated Discovery Layer
48% of teams are already optimizing GTM and content for AI-informed buyers and AI-mediated discovery, with another 28% in early experimentation. That means over three-quarters of the market is at least beginning to adapt to AI-shaped buyer journeys — vendors whose own product content, pricing pages, and proof points are not LLM-legible will be invisible to them.
Vendor Implication
Treat GEO and answer-ready content as a revenue function — structured product pages, comparison-ready pricing, and high-authority proof points.
OPPORTUNITY 06
Sell Into a More Skeptical, Evidence-Driven Buyer
Buyer conversations are clearly shifting toward more informed buyers and heavier proof requirements. Only the 18% still in early-stage AI use are likely to accept high-level promises; the rest expect benchmarks, references, and ROI math up front.
Vendor Implication
Equip sellers with evidence-rich talk tracks, ROI models, and third-party validation tuned for AI-informed buyers.
Chapter 01
AI Is Now Central to Sales and Marketing Workflows
“For integrator, it's five and five. We are very mature. We use it internally. We use it for our customers. And it's the central part of our strategy.”
— Strategic Data and AI Lead, Adecco Group
Listen
Finding 1.1
AI Adoption Maturity in Sales and Marketing Workflows
AI is moving into the core of sales and marketing operations, with 43% of respondents reporting moderate adoption across workflows and another 39% already deeply embedded. That means more than four in five organizations are using AI beyond experimentation, while only 18% remain in an early, selective stage.
Maturity shows up as workflow integration rather than isolated use. Moderately embedded teams are applying AI to call preparation, content drafting, targeting, and process automation, while AI-first organizations describe it as central to strategy. Early-stage teams, by contrast, still rely on ad hoc individual use without shared processes, limiting consistency and scale.
Key Takeaways
01
AI is now operationally mainstream: 84% report at least selective adoption, including 42% with selective or mid-stage use and another 42% where AI is highly embedded and strategically central across sales and marketing
02
Workflow integration has overtaken point solutions: 74% use AI in multi-step or default workflows, compared with 21% using it for task-specific augmentation and just 5% relying on point use or human-triggered assistance
03
Moderate embedding defines the current market: 43% say AI is moderately embedded across sales and marketing workflows, showing adoption has moved beyond experimentation but has not yet reached full maturity for most organizations
Strategic Implication
Shift go-to-market from “AI add-on” positioning to workflow transformation: package offers around integrated sales and marketing use cases, tier pricing by depth of embedding, and prioritize enablement that moves customers from task automation to default, multi-step orchestration. Segment accounts by adoption maturity, sell measurable operational outcomes to mid-stage adopters, and reserve premium strategic messaging, services, and expansion plays for organizations where AI is already central to execution.
AI Adoption Maturity in Sales and Marketing Workflows - Label Distribution
Moderately embedded across workflows43%
AI-first / deeply embedded39%
Early-stage / selective use18%
Listen
“Previously, call prep was mostly manual reviewing CRM notes, scheming past emails, checking LinkedIn, and doing quick web research. Now AI summarizes account history, surfaces key takeaways, highlights recent activity or intent signals, and suggests talking points before the call.”
VP overseeing product and go to market strategy
when asked about Listen: AI Adoption Maturity
Listen
“Probably a two. We've had access to Google Gemini for about a year now. And we're encouraged to actively use it in our day to day tasks. But there isn't any specific process that's been built where we have multiple teams following it throughout the business.”
Senior Product Marketing Manager, Enterprise Ed Tech
when asked about Listen: AI Adoption Maturity
Chapter 02
Teams Automate Prospecting and Admin, but Keep Relationship Selling Human
“I would fully automate the top of the funnel work. Which is, like, lead research, account enrichment, qualification, meeting scheduling, and follow ups because it is very repetitive and rule based task.”
— Consulting VP
Listen
Finding 2.1
Where Teams Draw the Automation Boundary in the Sales Cycle
85%
automate top-of-funnel and admin while keeping relationship selling human-led
Teams overwhelmingly place automation at the front and back ends of sales, not in the relationship core. About 85% favor automating top-of-funnel tasks and administrative work while keeping relationship selling human-led. Only 6% prefer selective human-in-the-loop automation across the process, and just 5% want core selling stages to remain primarily human with AI in a supporting role.
This boundary reflects a clear division of labor: AI is valued for speed, consistency, and background execution, while people are trusted for judgment, influence, and trust-building. In practice, teams want automation to absorb research, scheduling, reporting, and CRM updates so sellers can spend more time tailoring outreach and deepening customer relationships, especially in more complex or consultative sales environments.
Key Takeaways
01
Relationship selling stays firmly human-led: 85% draw the automation boundary at human-led relationship selling, using automation around the process but not for trust-building and deal ownership
02
Top-of-funnel automation is the dominant model: 77% automate prospecting and other early-stage tasks with human review or follow-through, while only 4% fully automate end-to-end outbound
03
Teams automate operations, not relationships: 93% automate workflow, admin, and transactional steps while keeping humans responsible for customer relationships, with just 4% limiting AI to a lighter support role only
Strategic Implication
Automate prospecting, data entry, routing, scheduling, follow-ups, and proposal workflows aggressively, but reserve discovery, objection handling, negotiation, and account ownership for sellers. Position AI as a rep amplifier, not a relationship replacement: price and package around productivity gains, faster pipeline creation, and lower admin burden rather than “autonomous selling.” Build operating models with mandatory human review at handoff points, and invest enablement in personalization, trust-building, and closing skills where differentiation still depends on people.
Where Teams Draw the Automation Boundary in the Sales Cycle - Label Distribution
85%
Automate top-of-funnel and admin, keep relationship selling human-led
Automate top-of-funnel and admin, keep relationship selling human-led
85%
Selective human-in-the-loop automation across the process
6%
Keep core selling and relationship-heavy stages human-led with AI only in support
5%
Automate broad workflow/admin/transactional steps while humans own relationships
2%
Broad end-to-end automation with limited human exceptions
2%
Listen
“So automation handles speed and efficiency, and humans win on confidence and judgment and influence.”
Sales Director or Chief Sales Officer
when asked about Listen: Automation Boundary
Listen
“I think with any AI now, I think what it's going to allow us to do from a sales perspective is allow us to have more networking abilities and more meaningful relationships with our customers and or prospects instead of having to worry about creating reports, updating reports, sending out promotional material.”
SVP in Business Banking, Large Mega Bank
when asked about Listen: Automation Boundary
Chapter 03
Trust and Judgment Keep Humans Central in AI-Augmented Sales
“But once it's a real deal, relationships become more important, and that's where when there's risk and there's complexity and multiple stakeholders and money on the line, people will still want to trust the person they're buying from.”
— Sales Director or Chief Sales Officer
Listen
Finding 3.1
The Human Role in an AI-Augmented Sales Model
Human-led relationship-building is the defining source of value in AI-augmented sales, cited in nearly four out of five comments on this theme. Another 15% point to human judgment and soft skills more broadly, while only 7% emphasize consultative selling in complex deals, underscoring that trust remains the clearest human differentiator.
Relationships matter most as sales split into transactional, AI-handled motions and higher-stakes, human-led engagements. Respondents consistently tie human value to credibility, comfort, and stakeholder alignment, especially when risk, complexity, and significant spend are involved. In practice, AI may accelerate workflows, but people still close consequential deals by creating confidence and navigating organizational dynamics.
Key Takeaways
01
Relationships and trust drive human value: 79% say human-led relationship-building and trust are the key human advantage in AI-augmented sales, with 45% explicitly naming relationships as the core differentiator
02
Judgment and soft skills keep humans central: 56% say human judgment and soft skills are the primary advantage, while 34% point to adaptability and tech-enabled consultative selling as the main human differentiator
03
Human presence matters most in critical moments: 44% say humans are most important in high-value or sensitive situations, while only 10% see AI as supporting engagement with humans still front-facing
Strategic Implication
Redesign sales coverage around a human-led, AI-assisted model: reserve top reps for high-value, complex, and sensitive moments, while using AI for research, preparation, follow-up, and workflow efficiency. Position pricing and messaging around consultative expertise, trust, and tailored judgment rather than speed alone. Invest in coaching for discovery, empathy, negotiation, and interpretation of AI outputs so sellers differentiate through relationships and decision support, not information delivery.
The Human Role in an AI-Augmented Sales Model - Label Distribution
Human-led relationship-building and trust78%
Human judgment and soft skills are the primary advantage15%
Human judgment and consultative selling in complex deals7%
Listen
“I think the relationships are gonna always be very important because you're gonna need that level of trust in being comfortable engaging with a new supplier or vendor.”
Head of Sales for Google Workspace, Western States, Google Cloud
when asked about Listen: Human Role in Sales
Listen
“But trust, credibility, accountability, and executive alignment will still require real human engagement.”
Consulting VP
when asked about Listen: Human Role in Sales
Chapter 04
AI Value in Sales Splits Between Scale and Quality
“We do use AI to write emails, the integration has vastly improved. The speed at which we can get to the market now, the rate at which we review emails and campaigns. It's probably cut our time in half.”
— Director of Sales and Marketing
Listen
Finding 4.1
How AI Value Shows up in Sales Execution
98%
of respondents said AI value shows up in sales execution
AI value is showing up clearly in sales execution, with 98% of respondents reporting some benefit. Nearly half, 47%, describe the payoff mainly as efficiency and productivity gains, while another 41% see both faster execution and better messaging or conversion. Only 10% report limited or unproven impact.
The strongest pattern is that AI reliably accelerates sales work, especially drafting, research, and campaign preparation, while conversion gains remain more modest and uneven. Roughly four in ten respondents report a dual benefit of efficiency plus stronger outreach, but the larger group points to speed first, suggesting AI is already improving execution at scale even when revenue impact is still emerging.
Key Takeaways
01
AI value in sales is nearly universal: 98% say AI value shows up in sales execution, making it one of the clearest areas where impact is already being realized
02
Scale is the dominant sales benefit: 52% report both efficiency gains and higher output volume, while another 43% see primarily time savings, leaving just 4% who experience only narrow task-level productivity
03
Quality gains lag behind volume gains: 40% see clear improvement in quality or engagement, but 48% describe the lift as perceived rather than proven and 8% say volume increased while quality or engagement stayed flat or worsened
Strategic Implication
Shift sales AI strategy into a two-track model: price and message the core offer around proven scale—faster rep execution, more outreach, and higher activity—while packaging quality improvement as a governed premium tied to coaching, content controls, and conversion analytics. Reallocate enablement toward workflow adoption first, then instrument engagement, reply, meeting, and close-rate measurement to validate lift. Position vendors and internal programs on measurable pipeline productivity, not generic claims of better messaging alone.
How AI Value Shows Up in Sales Execution - Label Distribution
47%
Primarily efficiency and productivity gains
Primarily efficiency and productivity gains
47%
Efficiency plus better messaging and conversion
41%
Limited or unproven sales impact
10%
Efficiency and better messaging and conversion
1%
Volume up but quality/engagement worse or unchanged
<1%
Listen
“Yes. We use AI to help draft outbound sales emails, mainly as a starting point rather than a final version. It's improved speed and consistency a lot. Reps can generate a solid first draft quickly and then personalize it. In terms of impact we've seen modest improvement in open rates and reply rates mostly because messaging is clearer.”
VP overseeing product and go to market strategy
when asked about Listen: AI Value in Sales
Listen
“I would say it allows for more production of content to be delivered. So the total number of emails delivered definitely increases, but still fighting a battle to improve open rates and click through rates.”
Fractional Chief Marketing Officer, Fintech
when asked about Listen: AI Value in Sales
Chapter 05
GTM Teams Adapt Fast to AI-Shaped Discovery and Buyers
“Yes. We are adjusting our content strategy. Because AI can easily summarize standard blog posts, we are putting less emphasis on generic thought leadership articles. Instead, we're leaning more into content that's harder for AI to replicate or commoditize — short form videos, webinars, POV driven opinion pieces from leaders and content based on proprietary data or unique customer insights.”
— VP overseeing product and go to market strategy
Listen
Finding 5.1
Adapting GTM Strategy to AI-Informed Buyers and AI-Mediated Discovery
48%
are actively optimizing GTM and content for AI-informed buyers and AI-mediated discovery
Teams are actively reshaping go to market strategy for AI mediated discovery, with 48% already optimizing content and buyer journeys for AI informed buyers. Another 28% are in early experimentation, while only one in five report no active adaptation, signaling that AI visibility is becoming a mainstream commercial priority.
The strongest responses go beyond classic SEO and focus on making product content legible to LLMs, while shifting investment away from generic thought leadership toward proprietary data, opinion led formats, video, and high signal assets. That split suggests leaders are treating AI discovery as both a content distribution challenge and a differentiation challenge.
Key Takeaways
01
Nearly half are retooling GTM for AI discovery: 48% are actively optimizing GTM and content for AI-informed buyers and AI-mediated discovery, showing this has already moved from edge experiment to mainstream response
02
Buyer conversations now demand stronger proof: 54% report clear shifts toward more informed buyers and heavier proof requirements, versus just 26% seeing little to no meaningful change in conversations
03
AI visibility tactics are becoming a competitive divider: 40% are actively experimenting with GEO and content adaptation, while 31% are only in selective or early-stage adaptation and 28% have made minimal changes or kept current content strategies
Strategic Implication
Rebuild GTM around proof, discoverability, and speed: arm sellers with evidence-rich talk tracks, ROI models, third-party validation, and objection handling for AI-informed buyers; redesign pricing and packaging to be simpler to compare and easier for AI-mediated research to interpret; and shift content investment toward GEO-ready assets, defensible category narratives, and high-authority proof points. Treat AI discovery visibility as a revenue lever, not a content side project, and close the gap before selective adopters lose share to faster competitors.
Adapting GTM Strategy to AI-Informed Buyers and AI-Mediated Discovery - Label Distribution
48%
Actively optimizing GTM and content for AI-informed buyers and AI-mediated discovery
Actively optimizing GTM and content for AI-informed buyers and AI-mediated discovery
48%
Early experimentation with GEO and content adaptation
28%
No active adaptation to AI-informed buyers or AI discovery
20%
Active experimentation with GEO and content adaptation
3%
Conversations clearly shifting to informed buyers and stronger proof
<1%
Some AI-shaped questions or misconceptions, but limited impact so far
<1%
Listen
“This is where we are looking into sort of revamping our website to make sure that prospects and customers can find our products, learn about our solutions, through AI tools like ChatGPT.”
Director of Product Marketing, Cybersecurity
when asked about Listen: GTM Strategy Adaptation
Questions & Answers
What Sales and Marketing Leaders Are Asking About This Study
Question 01 · Sample
How Many Respondents Are in This Sales and Marketing Study?
The study is based on 255 in-depth interviews with business professionals across technology, financial services, healthcare, retail, and manufacturing. All participants were selected for their direct experience with AI adoption in sales and marketing processes, and company sizes ranged from small businesses to large enterprises.
Question 02 · Adoption
How Far Has AI Adoption Progressed in Sales and Marketing?
AI adoption is well past the pilot stage. 43% of respondents say AI is moderately embedded across workflows and another 39% say it is deeply embedded, meaning 82% are using AI beyond isolated experimentation. Only 18% remain in an early, selective stage.
Question 03 · Automation boundary
Where Are Teams Most Comfortable Using AI in the Sales Cycle?
Teams are most comfortable using AI for top-of-funnel and administrative work. About 85% favor automating those areas while keeping relationship selling human-led, showing a strong consensus that AI should support execution without taking over trust-based buyer interactions.
Question 04 · Value
Where Is AI Delivering the Clearest Business Value Today?
The clearest value is in sales execution. 98% of respondents report some benefit, with 47% seeing mainly efficiency and productivity gains and another 41% seeing both faster execution and better messaging or conversion. Only 10% report limited or unproven impact.
Question 05 · Human edge
What Remains the Most Important Human Advantage in an AI-Augmented Sales Model?
Trust-based relationship-building remains the defining human advantage. 79% of respondents discussing this theme pointed to human-led relationships and trust as the key source of value, ahead of broader judgment and soft skills at 15% and consultative selling in complex deals at 7%.
Question 06 · GTM shift
Is AI Changing Go-To-Market Strategy as Well as Internal Workflows?
Yes. 48% of teams are already actively optimizing GTM and content for AI-informed buyers and AI-mediated discovery, and another 28% are experimenting. That means over three-quarters are at least beginning to adapt to AI-shaped buyer journeys.
Question 07 · Vendor lens
Why Should SaaS Vendors Care About These Findings?
The findings signal a clear repositioning opportunity for SaaS vendors: 82% of buyers already operate with AI moderately or deeply embedded, 85% draw the automation line at relationship selling, and 48% are reshaping GTM for AI-mediated discovery. Vendors that price and message around workflow orchestration, rep amplification, and AI-discoverable content will win share over those still selling AI as a feature add-on.
Strategic Recommendations
What This Means for You
01
Critical
Design Around Task-Level Augmentation, Not Full Automation
Formalize which sales and marketing tasks should be AI-led versus human-led, using the current market norm as a guide: automate scalable research, drafting, targeting, and admin work, but preserve human ownership of trust-based interactions. This aligns deployment with how value is actually being captured in practice.
02
Critical
Measure AI Success Through Rep Effectiveness
Shift KPIs away from labor removal alone and toward execution outcomes such as faster preparation, better messaging quality, higher response rates, and conversion lift. The strongest evidence of impact is in execution improvement, not relationship replacement.
03
High
Invest Deliberately in Human Trust Capabilities
As AI takes on more repeatable work, strengthen the human side of the commercial model through coaching on relationship-building, judgment, and high-stakes buyer conversations. This protects the core differentiator respondents most consistently identified as uniquely human.
04
High
Adapt Content and Discovery Strategy for AI-Informed Buyers
Audit GTM content for LLM legibility, structured product clarity, and answer-ready formats that support AI-mediated discovery. Organizations that connect internal AI maturity to external discoverability will be better positioned as buyer journeys continue to shift.
G2 Research
G2 is the world's largest and most trusted software marketplace.
Methodology
This research draws on 255 in-depth interviews with business professionals representing a wide mix of roles, industries, and company sizes.
Interviews ran 4 to 30 minutes and covered AI adoption maturity in sales and marketing workflows, how AI value shows up in sales execution, where teams draw the automation boundary in the sales cycle, and the human role in an AI-augmented sales model. The conversational format allowed respondents to discuss their actual practices rather than select from preset options, surfacing nuance that closed-ended surveys typically miss.
Respondents included business professionals across technology, financial services, healthcare, retail, and manufacturing. All participants were selected for their direct experience with AI adoption in sales and marketing processes. Company sizes ranged from small businesses to large enterprises.
The analysis of 255 interview transcripts was conducted using AI for semantic understanding, with multi-iteration validation and cross-verification to ensure analysis quality. Each transcript was independently reviewed by G2's AI Custom Research team to inform narrative, context, and clarity.
G2 Research, May 2026
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