This article was based on the interview with Asana CMO Prachi Gore on realizing AI’s true potential for marketing operations by Greg Kihlström, AI and MarTech keynote speaker for The Agile Brand with Greg Kihlström podcast. Listen to the original episode here:
We’ve been living in the generative AI era for a couple of years now. The initial thrill of generating a sonnet about SaaS pricing models in three seconds has, for most marketing leaders, given way to a more pragmatic and pressing question: where is the ROI? We’ve all seen individual productivity spike. A content writer can now produce three blog post drafts in the time it used to take for one. A campaign manager can brainstorm a dozen ad copy variations before their first coffee is finished. These personal productivity gains are real, and they are not insignificant. But for the enterprise leader looking at the overall velocity of the marketing organization, a troubling paradox has emerged.
While individuals are moving faster than ever, teams often feel stuck in the same place. The promise of AI-driven agility has, in many cases, manifested as AI-driven fragmentation. We’ve accelerated the creation of assets and ideas, but in doing so, we’ve inadvertently created more silos, more chaos, and more manual work stitching it all together. The copy-pasting between a chatbot window and a project brief, the “context engineering” required to get an AI up to speed, the sheer volume of uncoordinated output—it all points to a fundamental misunderstanding of the problem. The challenge was never a lack of ideas or content. The challenge has always been one of coordination. And as we’re learning, throwing individual productivity tools at a systemic coordination problem doesn’t solve it; it often makes it worse.
The Paradox: When More Output Doesn’t Equal Better Outcomes
The first hurdle for any marketing leader looking to operationalize AI is to acknowledge this gap between individual output and collective outcome. It’s a phenomenon that Prachi Gore, CMO at Asana, experienced firsthand before joining the company. Her team, like so many others, dove into AI experimentation and saw immediate results at the individual level. But when it came to the bigger picture of campaign velocity and team-wide productivity, the numbers didn’t move in the same way.
“My aha was that, you know, output just went up materially and outcomes didn’t follow that same path. And, like, that’s the paradox that you pointed out… There’s lots of data that says, you know, 75% of the knowledge workers are using AI, which is fantastic, uh, but only 5% of companies are seeing any real business outcome from it, are reporting any ROI.”
This disconnect is the central friction point for AI in the enterprise today. The productivity gains achieved by an individual using a generative AI tool are often lost in the “coordination tax” that follows. Gore describes a familiar scene: copying and pasting from one platform to another, manually translating feedback, and becoming a human glue between disparate systems. This isn’t a technological failure of the AI models themselves; it’s an operational failure. Workflows that matter—like launching a new product or a multi-channel campaign—are inherently complex. They cross teams, systems, and require handoffs, approvals, and shared context. When each person on that chain uses their own personal AI assistant in a silo, it can create more noise and disjointed artifacts, not less. The result is a surge in raw materials (ideas, drafts, assets) without a corresponding improvement in the factory’s assembly line. The bottleneck simply moves from creation to coordination.
The Solution: From Personal Assistants to AI Teammates
To solve this coordination problem, we need to evolve our thinking about AI’s role within our teams. The current model largely treats AI as a personal assistant—a one-to-one relationship between a human and a tool. It’s a “single-player” mode that enhances individual tasks. The future, however, requires a “multiplayer” approach, reframing AI not as a personal tool, but as an integrated teammate that can collaborate across the entire team and its workflows.
“It’s truly multiplayer in that one agent can work with multiple people in a system, following a plan… You are commenting on my doc to say, ‘Change this, blah, blah, blah.’ The agent is able to read it, make the edits, inform both of us, and keep the project moving forward. I think that unleashes, like truly, the ROI for any team.”
This concept of an “AI teammate” is a profound shift. Unlike a generic chatbot in a separate window, an AI teammate has an identity, permissions, and context within your team’s operating system. It can be assigned tasks, read comments, access relevant documents, and autonomously move a project from one stage to the next. Imagine a “Campaign Strategist” agent that doesn’t just help you write a creative brief, but then shares it with the relevant stakeholders, gathers their feedback directly from comments, revises the brief, and creates sub-tasks for the content, design, and web teams. This agent isn’t just a tool; it’s a participant in the workflow. This is how you move from simply generating more content to actually shipping campaigns faster. It transforms AI from a source of potential chaos into an engine of structured orchestration.
The Mindset Shift: Everyone is a Manager Now
Adopting this new model of AI teammates requires more than just new technology; it demands a significant shift in skills and mindset, both for leaders and for every individual contributor on the team. The old ways of working—and even managing—are insufficient in a world where we collaborate with intelligent agents. Gore points to two critical mindset changes that are essential for success.
“I think the biggest change in mindset is we have to think agents-first in everything… The second mindset shift is, at every level in the company, you’ve all become managers now.”
“Agents-first” is a simple but powerful directive. For any new task, the first question should be, “Can an agent do this?” This forces a re-evaluation of how we spend our time, pushing the automatable, process-driven work to AI so that humans can focus on what Gore calls “taste-making”—the unique strategic insight, creativity, and judgment that separates great work from average work.
The second shift, that “everyone is a manager,” is perhaps even more transformative. When you work with AI teammates, you are effectively managing a direct report. You must provide them with clear instructions (a “job description”), give them the right context (onboarding materials like brand guidelines or narrative documents), and guide their work. This requires a new level of clarity, structure, and strategic thinking from everyone in the organization. The value of human experience is not diminished; it’s amplified. A seasoned marketer’s ability to “prompt better, catch the things that don’t seem right, and guide the agents better” becomes their primary value-add. This is a far cry from simply using AI as a glorified spell-checker; it’s about becoming an architect and conductor of a human-AI hybrid team.
Orchestration is the New Execution
As we navigate the next phase of AI adoption, it’s clear that the conversation is moving beyond simple experimentation. The C-suite is no longer impressed by demos that generate clever ad copy; they want to see how this technology impacts growth, efficiency, and speed to market. The answer doesn’t lie in giving every employee a faster digital shovel. It lies in redesigning the entire construction site. The fragmentation and chaos many teams are experiencing is a symptom of applying a single-player solution to a multiplayer problem.
The leaders who will win in this new era are not the ones who simply adopt AI, but the ones who successfully operationalize it. This means shifting focus from individual output to collective outcomes, evolving from personal AI assistants to integrated AI teammates, and fostering a culture where every team member thinks like a manager of intelligent agents. The true competitive advantage of AI will not be found in the speed of creation, but in the intelligence of orchestration. It’s a coordination problem, and for the marketing leaders who solve it, the promise of 10x—or even 100x—growth is no longer hyperbole. It’s simply the new expectation.
