AI is changing the economics of go-to-market. Tasks that once consumed hours of a marketer’s or salesperson’s day—researching accounts, analyzing buying signals, developing content, personalizing outreach, and preparing for customer meetings—can now be accomplished in minutes.
The opportunity is enormous. But it raises a question that I don’t think enough GTM leaders are asking:
If AI can take on more of the work, where should we be investing our increasingly valuable human time?
The answer isn’t simply to keep a human in the loop at every step. That approach can create bottlenecks, undermine efficiency gains, and leave teams spending their time reviewing AI outputs rather than advancing revenue opportunities.
Instead, we need to be much more intentional about where human involvement makes the greatest difference.
The AI Productivity Paradox
A July 2026 McKinsey report, The Future of B2B Sales: How Growth Champions Rewire Their Playbooks with AI, highlights an important disconnect between AI adoption and commercial impact.
Based on McKinsey’s 2026 B2B Pulse Survey of nearly 4,000 buyers and sellers across 13 countries, the research found that while AI experimentation is widespread, fewer than 10% of organizations have scaled AI in any given function.
The difference between companies experimenting with AI and those realizing meaningful commercial value isn’t simply the sophistication of their technology. It’s how they’re redesigning their workflows.
McKinsey found that growth leaders are moving beyond individual productivity tools to rethink end-to-end commercial processes, enabling AI to handle more of the searching, synthesizing, drafting, and administrative work while sellers focus on relationship building, problem solving, and customer conversations.
The implication for GTM leaders is significant.
The goal shouldn’t be to automate as many activities as possible. It should be to create a GTM engine that generates more revenue from the combined capabilities of humans and AI.
And that requires some deliberate choices.
Not All Human Intervention Creates Equal Value
Consider two examples.
In the first, a marketing manager spends an hour reviewing and editing AI-generated outreach emails before approving them for distribution.
In the second, a sales leader spends that same hour reviewing AI-identified opportunities, determining which accounts have the greatest revenue potential, and developing strategies to engage key decision-makers.
Both represent humans in the loop. Both may be necessary.
But they don’t necessarily create the same commercial value.
The first primarily protects quality and reduces risk. The second has the potential to influence which opportunities enter the pipeline and how effectively they’re pursued.
Of course, quality control matters. Brand reputation, accuracy, compliance, and customer trust require appropriate safeguards. But those safeguards can increasingly be designed into automated workflows, with humans reviewing exceptions rather than every routine output.
That creates an opportunity to redirect human effort toward the decisions and interactions where experience, creativity, judgment, and relationships can materially change an outcome.
Five Places Where Human Judgment Can Have the Greatest Impact
1. Deciding Which Accounts Are Worth Pursuing
AI can analyze thousands of companies against an ideal customer profile, identify intent signals, monitor executive changes, review financial reports, and surface accounts that appear most likely to buy.
That’s an enormous improvement over manually building target lists.
But identifying an account with buying signals isn’t the same as knowing whether it’s worth pursuing.
A human might recognize that a company has a particularly compelling business problem, that an existing relationship creates an opportunity for a warm introduction, or that a recent organizational change makes the timing especially attractive.
AI’s role: Surface and prioritize opportunities based on available evidence.
Human’s role: Apply commercial judgment to determine where to invest finite Sales and Marketing resources.
The goal isn’t simply a better target account list. It’s a higher percentage of target accounts converting into qualified pipeline.
2. Turning Account Intelligence Into a Differentiated Strategy
AI can assemble an impressive account brief in minutes, pulling together CRM history, company news, leadership changes, competitive information, and potential buying signals.
It can even recommend messaging and next-best actions.
But the real value comes from translating that intelligence into a compelling reason for a buyer to engage.
Why should this company prioritize solving this problem now? What business outcome matters most to its leadership? What perspective can we bring that the buyer hasn’t already considered?
These are questions where industry expertise, customer understanding, and creative problem-solving become especially valuable.
AI’s role: Gather intelligence, connect signals, and recommend possible strategies.
Human’s role: Develop the differentiated point of view that gives a buyer a reason to engage.
The distinction matters. More personalized outreach doesn’t necessarily create more pipeline. More relevant, compelling outreach has a much better chance.
3. Creating Meaningful Buyer Conversations
AI can prepare a seller for a meeting, suggest discovery questions, summarize previous conversations, and identify potential objections.
It can also capture notes and recommend follow-up actions.
But complex B2B buying decisions are rarely driven by information alone.
They involve competing priorities, internal politics, organizational risk, personal motivations, and multiple stakeholders who may not agree on what success looks like.
A skilled salesperson knows when to ask another question, challenge an assumption, explore an unexpected concern, or help a buyer see a problem differently.
That kind of conversation can fundamentally change the direction of an opportunity.
AI’s role: Prepare sellers with context, insights, and recommendations.
Human’s role: Build trust, uncover motivations, shape solutions, and establish mutual commitments.
The measure of success isn’t how many meetings AI helps schedule. It’s how effectively those meetings move qualified opportunities forward.
4. Bringing Original Thinking Into Marketing
AI has dramatically reduced the cost and effort required to produce marketing content.
But when every company can generate a blog post, email sequence, or white paper in minutes, producing more content is unlikely to be a sustainable competitive advantage.
Original insight becomes more important.
Customer interviews, subject-matter expertise, proprietary research, and firsthand experience can uncover perspectives that aren’t readily available in the information AI models have already absorbed.
AI can help transform those insights into multiple formats, optimize content for search and AI discovery, and distribute it across channels.
But the underlying thinking still needs to be differentiated.
AI’s role: Accelerate research, drafting, optimization, repurposing, and distribution.
Human’s role: Develop original insights, challenge conventional thinking, and create a distinctive market perspective.
The opportunity isn’t simply to publish more. It’s to create content that earns attention, builds credibility, and opens conversations with prospective buyers.
5. Identifying and Advancing Customer Expansion Opportunities
The same principles apply after a deal closes.
AI can analyze product usage, customer engagement, organizational changes, support history, and whitespace to identify potential expansion opportunities.
It can recommend when to reach out and which additional solutions might be relevant.
But turning those signals into expansion revenue often requires a deeper understanding of the customer’s evolving business priorities and relationships.
A customer success leader or account executive may recognize an opportunity to introduce a solution to another department, engage a new executive stakeholder, or help the customer build a business case for a broader investment.
AI’s role: Continuously monitor customer accounts and surface expansion opportunities.
Human’s role: Deepen relationships, validate business needs, and co-create the path to expansion.
This is where an AI-powered GTM engine can support not just new customer acquisition, but sustained account growth.
A Better Framework: Automate, Augment, or Elevate
Rather than asking whether every activity should have a human in the loop, GTM leaders can evaluate workflows through three lenses.
Automate: Activities that are repetitive, rules-based, and low-risk, where AI can reliably execute within established guardrails. Examples include data enrichment, routine reporting, meeting summaries, and certain campaign operations.
Augment: Activities where AI can do the heavy lifting, but human judgment improves the outcome. Examples include target account prioritization, campaign strategy, deal analysis, and next-best-action recommendations.
Elevate: Activities where human expertise and relationships are central to creating value. Examples include executive buyer conversations, strategic solutioning, original thought leadership, complex negotiations, and customer expansion planning.
These categories aren’t absolute. The right level of oversight will vary based on risk, strategic importance, confidence in the data, and the potential impact of a mistake.
But the framework forces an important conversation: Where are we deploying humans because they’re truly adding value, and where are we involving them simply because that’s how the process has always worked?
Measure the Return on Human Time, Not Just AI Productivity
As AI becomes embedded in GTM operations, traditional productivity measures tell only part of the story.
It’s useful to know how many hours AI has saved, how many campaigns have been launched, or how much content has been produced.
But those metrics don’t tell us whether the business is growing.
What if we measured AI’s impact through a different set of questions?
- Are we generating more qualified pipeline per salesperson?
- Are more target accounts converting into meaningful sales opportunities?
- Are opportunities advancing faster through the pipeline?
- Are we improving win rates and average deal values?
- Are we identifying and converting more expansion opportunities within existing accounts?
These outcomes offer a better indication of whether AI is genuinely improving the GTM engine.
They also create a stronger basis for deciding where to reinvest the human capacity that automation frees up.
The Real Competitive Advantage Is How We Reinvest Human Capacity
McKinsey’s research reinforces an important point: Companies capturing meaningful value from AI aren’t simply layering new tools onto existing workflows. They’re redesigning how commercial work gets done.
For GTM leaders, that means thinking beyond automation and efficiency.
It means connecting Sales, Marketing, and Customer Success around shared account intelligence, clear priorities, and coordinated actions. It means designing workflows that deliver the right insights to the right people at the right time. And it means being intentional about where human judgment, expertise, and relationships can have the greatest commercial impact.
The companies that win won’t necessarily be those that automate the most work.
They’ll be the ones that make the smartest decisions about which work still deserves their people’s time.
So here’s the question I’d challenge every GTM leader to answer: If AI gave your team back 20% of its time tomorrow, where would you reinvest those hours to generate the greatest increase in pipeline and revenue?
Research reference: McKinsey & Company, “The Future of B2B Sales: How Growth Champions Rewire Their Playbooks with AI,” July 16, 2026.
