Atlassian Head of Sales breaks down what revenue organizations are getting wrong when deploying AI
Nick Feeney explains how AI helps revenue teams make better decisions by redesigning workflows, accelerating learning, and keeping human judgment at the center.
Operating Shift: Atlassian transformed AI from a productivity aid into a management system that improves deal decisions.
Human Judgment: AI processes revenue data, but people still handle trust, strategy, negotiation, and complex commercial decisions.
Connected Workflow: An end-to-end AI workflow prepares account briefs, detects risks, updates CRM records, and recommends next actions.
Faster Learning: AI compresses customer feedback loops, helping sales, product, marketing, and leadership respond to patterns sooner.
Deployment Discipline: Successful AI adoption requires measurable outcomes, connected data, redesigned workflows, and leaders who model change.
Nick Feeney is Head of Sales for Atlassian's marquee products, including Jira, Confluence, Loom, and Rovo.
We sat down with Nick to learn how industry leaders like Atlassian are changing sales workflows with AI. Here's what he told us.
Leading revenue for Atlassian's marquee products
My name is Nick Feeney, and I lead revenue at Atlassian for its first bundle, Teamwork Collection, encompassing its marquee products, Jira, Confluence, Loom, and its usage-based AI solution, Rovo.
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Atlassian is a ~$6.2 billion global company that continues to deliver strong growth, with revenue up 32% year over year, fueled by accelerating cloud adoption and AI expansion.
My organization spans AMER and LATAM, supporting high-touch customers across the mid-market, enterprise, and strategic segments. We partner with customers on complex digital transformation initiatives, leading value-based engagements ranging from higher-velocity five- to six-figure opportunities to multi-year, eight-figure enterprise and strategic partnerships.
In FY26, the Teamwork Collection business achieved 156% of its annual bookings target and delivered 132% net renewal expansion, making Teamwork Collection one of Atlassian’s fastest-growing and most strategic revenue-generating teams.
Throughout my career, I’ve been drawn to moments of platform transformation, where technology fundamentally changes how businesses operate and where GTM strategy has to be reinvented. That journey began at ToutApp in 2013, when sales productivity began reshaping modern GTM organizations. For the first time, revenue teams could move beyond a cold-calling-first approach, leveraging automated, personalized email sequences to engage prospects at scale. It marked a meaningful shift in pipeline generation, replacing manual, repetitive work with technology that enabled sellers to focus on higher-value customer interactions. That was one of the earliest waves of sales productivity software.
Since then, the category has expanded dramatically, with AI-driven platforms now helping teams identify high-propensity accounts, prioritize opportunities, automate workflows, and deliver far more personalized customer engagement. Having a front-row seat to that evolution reinforced a lesson that has shaped my career: The highest-performing revenue organizations embrace AI early, adapt their operating model quickly, and continually reinvent how they create value for customers.
Along the way, I worked at Mural during its earliest building stages. We helped define an entirely new category during the pandemic, as organizations learned to collaborate digitally at scale. From there, I joined Loom as workplace communication evolved further. And that experience ultimately led me to Atlassian.
The common thread has never been primarily the products themselves; it’s been helping organizations navigate fundamental shifts in how people work, as technology, customer expectations, and commercial strategy evolve simultaneously. I believe AI represents the next and largest chapter in that evolution, exactly the kind of transformational moment that has defined my career.
How AI can go from a productivity tool to a decision-making system
AI isn’t replacing great sellers or great leaders. It’s augmenting their judgment so every person can operate with greater context, consistency, and leverage…The results are limitless.
The biggest change we made within Atlassian's revenue organization wasn't just adopting AI; we fundamentally changed how we operate the business. A year ago, we primarily viewed AI as a productivity tool to help sellers work faster. Today, we use it as a management and decision-making system.
Historically, sales managers spent enormous time gathering information, inspecting CRM hygiene, reviewing call recordings, and preparing for forecast and pipeline reviews. AI now performs the initial layer of that manual analysis. Before a leader steps into a deal review, AI synthesizes customer conversations, surfaces deal risks, identifies stakeholder gaps, highlights buying signals, and summarizes the most important actions.
This fundamentally changed the role of our leaders and sellers. Instead of spending their time collecting information, leaders are coaching on strategy, strengthening deal quality, pressure-testing account plans, and helping sellers navigate executive conversations. Our operating cadence has shifted from inspecting activity to improving decisions. Similarly, sellers spend 90% of their energy driving value with customers, deeply understanding their pain, and dedicating resources fully to providing a solution.
AI isn’t replacing great sellers or great leaders. It’s augmenting their judgment so every person can operate with greater context, consistency, and leverage. The organizations that win won’t necessarily use the most AI, but they will make better decisions because AI continuously surfaces the right insights at the right time. The results are limitless.
An end-to-end workflow for customer conversations
Our team is using AI daily. Here's a workflow we use with customers.
AI synthesizes previous customer conversations and opportunities, product usage, key use cases, stakeholder relationships, and external company news (10-K reports, etc.) into a concise account brief. Instead of spending an hour gathering context, the seller begins with a comprehensive understanding of the customer’s business, recent activity, and likely priorities.
During the sales cycle, AI continuously analyzes customer conversations to identify buying signals, competitive threats, unanswered objections, key decision-makers we need to engage, and next-best plays to run. Rather than simply recording meetings, it helps surface risks while there’s still time to influence the outcome.
Following each interaction, AI automatically summarizes the meeting, updates CRM fields, drafts follow-up communications, recommends actions against our mutual action plan, and highlights changes to forecast confidence. This allows sellers and managers to spend less time documenting work and more time advancing the opportunity.
Why AI should prepare revenue decisions while people make them
Nick Shares
The best revenue organizations increasingly clarify where AI adds the most value and where human judgment remains irreplaceable…So, my thinking is simple: For strategic work, AI should prepare decisions, but people should make them.
The best revenue organizations increasingly clarify where AI adds the most value and where human judgment remains irreplaceable.
We rely heavily on AI for information processing and pattern recognition. We do much of this within our own solutions, like Rovo. It helps us prioritize pipeline, summarize customer interactions, identify deal risk, surface buying signals, highlight gaps in stakeholder engagement, improve forecast quality, and recommend where our teams should focus their time. AI excels at synthesizing thousands of data points into actionable insights far faster than any individual can.
We continue to rely on people for decisions requiring judgment, trust, and creativity. Customers don’t buy software because an algorithm recommended the next step. They buy because a seller/specialist can align diverse stakeholders, navigate organizational dynamics, negotiate complex tradeoffs, build executive relationships, and inspire confidence in a strategic vision. People want to buy from people. I'm confident that will never change.
Likewise, pricing strategy, hiring, organizational design, territory planning, and major commercial investment decisions ultimately require context, experience, and accountability that AI cannot provide.
So, my thinking is simple: For strategic work, AI should prepare decisions, but people should make them.
How AI boosts organizational learning velocity
The biggest result we’ve seen isn't a traditional sales metric; it's organizational learning velocity. Customer feedback, for example, historically moved incredibly slowly. A seller would have a conversation, capture a few notes, share them with their manager, and that insight might eventually reach product or marketing weeks later. By then, the context was often lost.
AI has dramatically compressed that feedback loop. AI now synthesizes customer conversations at scale, allowing us to identify recurring objections, competitive trends, feature requests, and buying patterns almost in real time. Instead of relying on anecdotes from a handful of deals, we’re learning from thousands of customer interactions simultaneously.
The impact extends well beyond sales. Product teams gain faster visibility into emerging customer needs. Marketing can refine messaging based on actual buyer language. Revenue leaders can identify where deals consistently stall and adjust enablement or processes before it becomes a systemic issue, or quickly adjust forecasts and support risk-mitigation strategies on deals, all without even connecting with their team directly.
This is one of many ways AI has reduced the intensive manual labor that used to suck up the majority of time for account executives and leaders. To me, that’s one of AI’s most underappreciated benefits. It doesn’t just make individual sellers more productive. It makes the entire company learn faster.
How AI has shifted competitive advantages
Information itself is no longer a competitive advantage….The new competitive advantage lies in how well a revenue organization interprets information, asks better questions, builds trust with executive stakeholders, and translates insights into business outcomes.
Information itself is no longer a competitive advantage.
For years, great sellers differentiated themselves because they had better research, knew the customer’s business more deeply, or uncovered insights that others couldn’t. Today, AI has democratized access to information. Every seller can generate account research, summarize earnings reports, analyze a prospect’s technology stack, or prepare for an executive meeting in minutes. That means information is no longer scarce. Judgment is.
The new competitive advantage lies in how well a revenue organization interprets information, asks better questions, builds trust with executive stakeholders, and translates insights into business outcomes. AI can help every seller become better prepared, but it can’t replace credibility, strategic thinking, or the ability to navigate complex organizational dynamics.
How organizations can deploy AI effectively
AI rarely fails because of the technology. Organizations usually fail because they haven't changed their work methods. Research from Forrester reinforces this. While AI adoption has accelerated rapidly, relatively few organizations can directly connect those investments to measurable financial outcomes.
The challenge often isn't model quality but organizational readiness. Here are some tips:
Organizations should focus on the business outcome before the technology. If you can’t define the commercial metric you’re trying to improve—whether that’s win rate, sales-cycle length, time to market, code quality, market expansion, forecast accuracy, or customer retention—it’s almost impossible to prove value.
AI should not be deployed as another standalone application. The greatest value comes when AI has access to the full context of the business across CRM, product usage, customer conversations, support interactions, and internal knowledge. Without that connected system of work, insights remain fragmented.
Organizations should stop optimizing for individual productivity. They should be redesigning workflows instead. Saving a seller ten minutes doesn’t necessarily create enterprise value. Reimagining how revenue teams qualify opportunities, coach deals, forecast, and collaborate does provide quantifiable, team-level results. The strongest results come when AI can draw on context across the organization rather than operating within isolated teams.
Organizations must stop underestimating change management. AI adoption is as much a leadership challenge as it is a technology initiative. Teams need new operating rhythms, clear accountability, and confidence in how AI fits into their daily work before behavior truly changes.
Ultimately, AI is not a strategy. It’s an accelerator. It amplifies the quality of the existing operating model. Organizations with disciplined processes, connected data, and clear commercial outcomes tend to realize outsized returns. Those without that foundation often end up automating inefficiency rather than creating a competitive advantage.
How to know if a potential hire is ready for AI-enabled work
Nick Shares
Today, the highest-leverage people are deeply curious, AI-native, and committed to continuous learning. They don’t just adopt new technologies. They constantly rethink how they work, automate repetitive tasks, and elevate the value they bring to customers.
The role of a revenue leader has fundamentally changed in the AI era. Success is no longer defined solely by operational excellence or sales execution. It’s defined by an organization’s ability to learn faster than the market. Today, the highest-leverage people are deeply curious, AI-native, and committed to continuous learning. They don’t just adopt new technologies. They constantly rethink how they work, automate repetitive tasks, and elevate the value they bring to customers.
As a leader, my responsibility is to build an organization around those qualities. That starts with hiring people who are adaptable, intellectually curious, and energized by change.
That's why, in every interview, I ask candidates: "What are the most manual processes in your day?" Then, if they can't tell me why they haven't automated those manual processes with AI, it's clear to me that they're not problem solvers, nor are they willing to be students of AI to improve how they work.
Six things every CRO must do to succeed with AI
So, here's my advice:
Resist the temptation to chase every new AI capability. The technology is evolving faster than any organization can keep up with, so your competitive advantage won’t come from having access to the newest AI craze. It will come from building an organization that can adapt faster than everyone else.
The first priority is talent density. That has never changed. But AI raises the ceiling for great people while also widening the gap between top performers and everyone else. Hire people who are deeply curious, intellectually humble, and committed to continuous learning. What works today may be obsolete in six months, so adaptability is becoming one of the most valuable traits in go-to-market.
Lead from the front. You can’t expect your organization to embrace AI if you’re not using it yourself. The best leaders aren’t delegating AI adoption. They’re demonstrating it, sharing prompts, experimenting publicly, and creating a culture that celebrates learning more than having the right answer.
Invest in clarity, not complexity. Buyers are overwhelmed by AI messaging, and most vendors sound interchangeable. Your sales team should be able to explain, in simple language, what problem you solve, why you’re different, why customers should act now, and how similar customers find the results they're looking for. If your differentiation can’t be understood in a few minutes, it probably isn’t clear enough.
Redesign workflows instead of simply adding tools. Too many organizations layer AI onto existing processes. The biggest gains come from rethinking how work is done, eliminating unnecessary steps, and allowing people more time for judgment, creativity, and customer relationships.
Finally, remember that AI doesn’t replace leadership. If anything, it makes leadership more important. As information becomes increasingly abundant, a leader’s role shifts from providing answers to setting direction, exercising judgment, and helping teams focus on what matters most. Technology will continue to evolve, but great leadership, customer empathy, and exceptional talent remain the enduring competitive advantages. You got this.
…here’s my advice: Resist the temptation to chase every new AI capability. The first priority is talent density. Lead from the front. Invest in clarity, not complexity. Redesign workflows instead of simply adding tools. Finally, remember that AI doesn’t replace leadership. If anything, it makes leadership more important.
