Summary
Artificial Intelligence (AI) rewired how go-to-market (GTM) works. Pipeline, trust, seller confidence, and revenue remain the revenue leader’s job to own.
AI has changed how revenue teams research buyers, read signals, and support sellers. The obligation to create pipeline, earn trust, and grow revenue has not moved.
Gartner’s 2026 CMO Spend Survey found that chief marketing officers (CMOs) now direct 15.3 percent of marketing budgets to AI, while only 30 percent describe their organizations as ready to scale those investments. The ambition runs well ahead of the readiness. Seventy percent of CMOs call becoming an AI leader a critical goal for 2026, and 70 percent also admit their internal processes are too immature to scale AI well.
Access to AI no longer impresses anyone, because access has become ordinary. The harder question is whether AI improves the quality of commercial decisions.
That is the central shift. AI changed the mechanics of GTM work, while revenue leaders still answer for pipeline, seller confidence, positioning, trust, and growth. The advantage will go to teams that rebuild their GTM operating system around buyer signals, account intelligence, seller judgment, customer proof, and measurable revenue. Tool count is the least of it.
The buyer journey is more digital and still deeply human
Artificial Intelligence (AI) has accelerated buyer research. It has also raised the value of human confidence at the decision point.
Gartner found that 45 percent of business-to-business (B2B) buyers used generative AI during a recent purchase, mostly to research vendors and products, and that buyers drew on an average of seven information sources. Even with that autonomy, the human role held firm. Sixty-nine percent of B2B buyers said they prefer to validate AI-generated insights with a sales representative.
Read those findings together, and the picture is consistent rather than contradictory. Buyers want autonomy while they research. They want reassurance as the risk rises.
The seller has lost the role of first information source. The seller is gaining a more valuable role, which is the source of validation, context, and confidence. Sales cannot validate weak messaging, thin proof, or generic claims, so marketing has to prepare the ground that makes validation possible.
Signal work needs an operating model for this reason. A single click, visit, or email open rarely tells the full story. Patterns across channels and weeks create the context sales can use.
AI raised the penalty for weak sales and marketing alignment
Artificial Intelligence (AI) has made every gap in the go-to-market (GTM) system more visible. When buyers draw on more sources and more stakeholders, inconsistency stops hiding. Conflicting positioning, disconnected content, and vague proof create friction the buyer feels directly.
The buying group also keeps expanding. Forrester’s The State Of Business Buying, 2026, based on a survey of nearly 18,000 buyers, found that an average purchase involves 13 internal stakeholders and nine external participants. The pattern intensifies with newer technology. For purchases that include generative AI features, the buying group doubles to 14 members, compared with seven for purchases without such features.
Two more findings raise the bar on proof. Forrester reported that procurement now serves as a decision-maker in 53 percent of business buying cycles. It also found that more than 60 percent of buyers purchase some form of trial, while only about a third plan to convert to a fully paid version with the same provider.
The buying group is larger, more skeptical, and more proof-driven. Marketing has to equip sellers with sharper context, and material alone will not do it.
A modern GTM motion answers four questions, repeatedly:
- Which accounts deserve attention now?
- Which signals suggest they may be ready?
- Which proof reduces their sense of risk?
- Which action should the seller take next?
The advantage moved from activity volume to commercial architecture
The market has stopped rewarding go-to-market (GTM) teams for sheer activity. The reward now follows integration.
McKinsey’s 2026 Global B2B Pulse Survey drew on nearly 4,000 decision-makers across 13 countries. Buyers now use an average of 10 channels across the journey, and 71 percent of B2B companies offer e-commerce.
The performance gap is stark. McKinsey found that 60 percent of market leaders report double-digit revenue growth, compared with 21 percent of laggards. Leaders behave differently in three measurable ways. They are four times more likely to deploy one-to-one personalization. They are twice as likely to have adopted generative AI. They more often place account-based marketing (ABM) ownership inside the sales process.
The synthesis is clear. Leaders pull ahead by connecting customer data, buyer behavior, personalization, ABM governance, and revenue accountability into one operating system. Integration is the differentiator, and McKinsey’s data makes that case directly.
AI agents matter only when they improve the workflow
Artificial Intelligence (AI) agents can support go-to-market (GTM) work, and they earn their value only inside a real workflow.
McKinsey estimates that agentic AI will come to power as much as two-thirds of current marketing activities. When teams rebuild the work around those agents, the upside is large. McKinsey reports that organizations implementing agentic workflows can expect 10 to 30 percent revenue growth from hyper-personalized marketing, and that agentic systems can accelerate campaign creation and execution by 10 to 15 times.
McKinsey’s guidance on how to get there is concrete. Leaders map the work at the task level, define agent roles, integrate with systems of record, and rebuild workflows around business goals. The same research adds a sober note for any leader tempted by the headline figures. Nearly 90 percent of CMOs are experimenting with AI use cases, while fewer than 10 percent have captured value across end-to-end workflows.
That gap explains the operating challenge. Agents need context, task boundaries, quality controls, and human review. Without that structure, they generate activity that no one can account for.
My own agent work has stayed close to support tasks. That includes signal interpretation, buying group expansion, account intelligence, sales brief drafting, and human review. The aim is better-informed seller judgment, applied with more consistency.
Next best action is where AI becomes revenue-relevant
Artificial Intelligence (AI) becomes revenue-relevant when it helps a team decide what to do next.
Gartner found that sales organizations giving sellers AI-enabled next best actions are 2.6 times more likely to achieve commercial growth, and that organizations prioritizing seller AI upskilling are 2.4 times more likely to report strong revenue growth. The shift is accelerating fast. Gartner predicts that by 2027, 95 percent of sellers will begin their research with AI, up from less than 20 percent in 2024.
The same research clarifies where human sellers still win. Gartner found buyers were 28 percentage points more likely to say a representative, rather than generative AI, helped them advance to the next step. They were 32 points more likely to feel confident, and 39 points more likely to feel understood.
Enablement has to follow the seller into the moment. A battlecard sitting in a folder cannot help a representative in a live conversation. The stronger model embeds next best action and proof inside the seller’s daily tools, which is exactly where McKinsey’s leaders place them.
AI can watch more signals than any team can track. People still decide what deserves belief, escalation, and a customer-facing move.
AI makes measurement harder when the operating model is weak
Artificial Intelligence (AI) can manufacture more activity, more content, and more dashboards. None of that guarantees better measurement.
Deloitte’s 2026 AI Pulse Check, which polled nearly 3,700 professionals, found that 48 percent introduced AI without redesigning the workflows or roles around it. Only 12 percent reported redesign at scale with a new operating model behind it.
Deloitte’s State of AI in the Enterprise, 2026, sharpened the same point. Only 30 percent of organizations are redesigning key processes around AI, and 37 percent use it at the surface with little change underneath. A further 34 percent are starting to use AI to deeply transform.
Leaders need measurement tied to business outcomes, and AI usage is the wrong yardstick. A dashboard that counts AI outputs measures motion, not progress.
The right dashboard asks a harder question. Did the GTM system improve account prioritization, conversion, velocity, seller confidence, proof quality, and revenue?
The new GTM operating model turns signals into action
The central go-to-market (GTM) question has changed. The old version counted campaigns. The better version measures how well a team converts market signals into timely, credible, revenue-relevant action.
I use a simple model for this, built from weekly operating practice. It follows a signal from first detection to closed-loop learning, across three stages: Sense, Decide, and Act.
Stage one, Sense.
- Collect signals from email engagement, website visits, paid media, customer relationship management (CRM) activity, intent data, events, and sales interactions.
- Normalize the signals by removing noise and grading source quality.
- Compare activity across weeks, so the team reads patterns rather than single events.
Stage two, Decide.
- Classify each account by separating curiosity, engagement, active research, and sales-worthy intent.
- Map the likely buying group, including decision-makers, influencers, technical evaluators, and blockers.
- Apply human review to validate the logic, remove false positives, and protect brand trust.
Stage three, Act.
- Generate a sales brief that turns the account story into a usable field asset.
- Recommend the next best action, whether outreach, a content asset, or a specific proof point.
- Capture the feedback loop by learning which signals led to conversations, opportunities, and revenue.
In my own weekly operating practice, I bring marketing, sales, advertising, website, and intent data into one analysis. The output tells sellers which companies deserve attention, what pattern they show, and what to understand before outreach. The aim is a better decision model for sales and marketing, with judgment kept firmly in human hands.
The VP of marketing role is becoming a revenue operating role
Artificial Intelligence (AI) has widened the marketing leader’s job. McKinsey describes the chief marketing officer (CMO) role as expanding from brand and demand stewardship to the orchestration of data, technology, and AI-enabled execution. Gartner shows heavy AI investment running ahead of readiness. Deloitte frames the core challenge as the distance between AI deployment and AI transformation.
The vice president (VP) of marketing now connects market understanding, positioning, pipeline strategy, sales enablement, customer proof, AI workflows, and measurement into one accountable system.
AI changed the tools, the speed, and the buyer’s access to information. It changed how sellers research, prepare, and follow up. The job of revenue leadership has held its ground. Revenue leaders still have to create demand, earn trust, sharpen focus, help sellers win, and prove business impact.
The strongest teams will turn AI into better commercial decisions.
So the question for any revenue leader is direct. When AI touched your go-to-market system this quarter, did your commercial decisions get sharper, or did your team simply get busier?
