Marketing hits its lead goal. Sales hits its revenue number. Customer Success hits its renewal target. And the customer can still have a lousy experience.
You wouldn’t call a basketball team successful because every player hit an individual stat while the team lost. A point guard can lead in assists, a center in rebounds, and a forward in points, and they can still lose to a less talented team that plays better together. Players still have individual roles and stats, but the team is also judged on the outcome. We understand that in sports. At work, we often reward each function for hitting its own number even when the customer experience suffers.
Nobody has to be doing a bad job. Each team can be doing exactly what it was asked to do. The customer moves across Marketing, Sales, and Customer Success, while our goals, systems, processes, and incentives often stop at the functional boundary.
For years, some of those gaps were hard to fix. Customer information sat in different systems, context got lost in handoffs, and connecting what one function knew to the next took more time and coordination than teams could sustain. AI can remove more of those constraints.
As it does, the human questions get harder to avoid. Will we trust what we learn? Will we act when the evidence challenges something we believe? Will Marketing, Sales, and Customer Success change how they work when a better customer outcome requires different choices?
Quick Take
AI can solve more of the mechanics behind disconnected GTM work. What I’m seeing is how quickly that exposes the harder human work.
- AI can carry customer and market context across functions. People still have to decide where functional accountability ends and shared accountability begins.
- Better information only helps when people trust it enough to act, especially when it challenges an existing belief or decision.
- Reimagining a workflow can change roles, ownership, decision rights, and how teams work together.
- I’ve seen AI improve a Marketing-to-Sales handoff enough to move SDR open rates from 15% to 40%. The workflow improved, but the teams still had to make the organizational decisions around it.
- Companies start from different places. History, hierarchy, culture, regulation, resources, and what they need to protect affect how quickly the human side can change.
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AI Can Solve More of the Handoff Now
I’ve seen this with a global cybersecurity company I work with. One workflow gave SDRs consistent company, industry, role, and messaging context instead of requiring them to recreate it for every prospect.
Reps saved one to three hours a day, and open rates increased from 15% to 40%.
AI solved an important part of the handoff. The workflow could carry context, but Marketing and Sales still had to decide what they should optimize for together, which customer signals to trust, and what to change when new evidence challenged how they were working.
Those are the kinds of human questions I keep seeing as more of the mechanics get easier.
Where the Human Work Shows Up
In my work with GTM teams, I’ve observed three key human challenges. They aren’t new, and I don’t think they’re the only ones. But as AI makes more of the mechanics easier, they’re becoming harder to work around.
1. Better Information Doesn’t Create Shared Outcomes
AI can make customer context much easier to share. That still doesn’t align what each team is accountable for.
Marketing can see what happens after a lead becomes an opportunity. Sales can know more about what brought the buyer in. Customer Success can start with more of the history. But if Marketing is measured on leads, Sales on the close, and Customer Success on renewal, each team can make a rational decision for its own number that works against the customer outcome.
The measures don’t need to be identical, and I don’t think the answer is to abandon functional goals. Sales still needs a quota. Marketing and Customer Success need measures tied to their own responsibilities. The harder question is where to add shared accountability so each function also has a stake in the customer outcome.
That could mean a shared goal, a common scorecard, or in some cases incentives tied partly to outcomes such as getting the right customers, time to value, retention, expansion, or overall customer satisfaction.
2. More Insight Requires More Trust
AI can synthesize far more customer and market information than any one person can process. The challenge comes when the evidence contradicts something we’ve already invested in.
- Customer conversations may challenge positioning we’ve spent a year building.
- Win/loss analysis may suggest the problem isn’t sales execution.
- Support tickets may show that something we considered minor is affecting customers far more than we realized.
Trust doesn’t mean accepting AI output at face value. We still need to check the evidence, add context, apply judgment, and decide whether it’s strong enough to act on.
It also requires trust between people. Someone needs to be able to bring uncomfortable evidence into a cross-functional conversation without it becoming an attack on someone’s work.
Curiosity and intellectual humility become more valuable as AI gives us more ways to test what we think we know. That only helps if we’re willing to be wrong and change our minds when the evidence supports it.
3. Reimagining Work Means Letting Go of Some Old Work
Changing a workflow gets harder when it changes who does the work, who owns the decision, or where someone’s expertise is needed.
People have spent years building expertise, careers, and influence around existing roles and processes. When AI makes a different way of working possible, we may be asking them to reconsider work they’re good at, responsibilities they own, or where they believe they add value.
That uncertainty deserves to be taken seriously. But if work that once required several people, multiple handoffs, and approval steps can now happen differently, we should be willing to rethink the process.
Stacy Simpson, CMO of athenahealth, is working through this now. Her team mapped the full campaign process and identified where individual agents could take on parts of the work. They’re now at the point where the process itself can change.
“We’re in the middle of the story. We know the outcome we want, but the process isn’t predetermined. We started by identifying where individual agents could help. Now we’re asking how the process itself should change, how agents hand work to each other, and where humans need to step in.”
Changing the process also changes what we need from the people doing the work. Siddharth Taparia, Global CMO of JLL is thinking about that from the role and talent side.
“AI fundamentally changes what we ask people to do and the skills they need. For example, a campaign manager will spend less time building campaigns and more time orchestrating and evaluating AI outputs. That means we have to rethink how we develop talent. The skills that matter most are shifting. Strategic thinking, cultural awareness, curiosity, and adaptability will become critical.”
We may not know exactly what every role becomes yet. That makes it more important to involve people in redesigning the work and help them see where their judgment, experience, and relationships can add more value.
I went deeper on five examples in 5 Reimagined Workflows: Work We Skipped That AI Now Does.
Different Starting Points
Organizations are starting from very different places. An established company may have customers, revenue, specialized roles, approval layers, regulatory requirements, and systems that already work. Changing any of those carries real risk.
A smaller company may have fewer people, less budget, and less technical support, but also less to unwind. When adding people isn’t an option, there can be more reason to rethink how the work gets done.
I think what a company has to protect may be a bigger factor than size. The more that already works, the more carefully people have to decide what should change and what should stay.
I covered this topic in more detail in The Boldest AI Moves are Coming From the Smallest Budgets.
Where Competitive Advantage May Come From
AI capabilities will keep advancing, and access to similar models and tools will get easier. The difference I’m watching is what companies do when AI gives them better information or makes a different way of working possible.
Over time, a company willing to change how the work gets done learns more than how to use AI. Its people learn where AI works well, where human judgment is needed, and how to change when new evidence gives them a reason to.
That’s where I think some of the advantage comes from. Technology can be bought. Trust, judgment, shared accountability, and the ability to change how people work together take longer to build.
Michael DiPietro, an operating advisor at JMI Equity who works across its portfolio companies, keeps the test grounded in business results:
“We encourage our portfolio companies to focus their AI initiatives on high-impact workflows and change. These should not only drive better business outcomes but ideally create a better customer experience. As one of our portfolio company leaders said, ‘It’s not AI transformation, it’s business transformation.’ We want them to think of AI as a means, not an end.”
I also don’t think this stays a competitive advantage forever. Over time, a more connected, context-aware experience may simply become something customers expect.
Customers don’t care how our org chart works. They expect us to know what they’ve already told us, carry that context across the journey, and respond as one company. As AI makes more of that possible, some of the gaps we’ve learned to live with become harder to justify.
I wrote more about the CMO’s role in carrying that customer context across the business in CEOs Are Looking for a Different Kind of CMO. Are You It?
Think big, start small, move fast. Try something, learn, adjust, and be willing to zig and zag.
A Few Questions I’d Ask
These are three questions I’d bring into a GTM leadership conversation:
- Shared outcomes – Where should Marketing, Sales, and Customer Success keep distinct goals, and where should they share accountability for the customer outcome?
- Trust – What happens when customer or market evidence challenges something an influential person or function believes?
- Willingness to change the work – If we designed this work from scratch today, would we give the same people and functions the same responsibilities and decision rights?
What I’m Still Figuring Out
What I’m still trying to understand is what helps people move from agreeing that work should change to being willing to change their own work.
Hands-on experience seems to help. I see belief change when people use AI on their own problems, see what it can and can’t do, and have a hand in redesigning the workflow.
I’m also watching what happens when the next role isn’t clear yet. People may be more willing to let go of familiar work when they can see where their judgment, experience, and relationships will add more value. But we won’t always have that answer before the work starts changing.
We’re asking people to help redesign work that may also change their own roles. I think they need a real hand in shaping what comes next.
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