Work Sprawl and the Future of Marketing with Global VP Marketing @ ClickUp, Kyle Coleman

AI transformation, category ownership and the evolving mandate of marketing

Here’s a counterintuitive truth that will make most CMOs uncomfortable: the mandate for marketing leadership hasn’t changed in the AI era—but the resource equation has become so dramatically different that clinging to old operating models is professional suicide.

I recently sat down with Kyle Coleman, Global VP Marketing at ClickUp, whose 16-year journey from SDR to senior marketing leadership spans some of the most transformative companies in B2B SaaS. Kyle spent 6 years at Looker through its acquisition by Google, 5 years at Clari where he became CMO, a stint at Copy.ai as CMO, and now ClickUp. What emerged from our conversation wasn’t just another “AI will change everything” platitude. Instead, Kyle articulated something far more nuanced: a complete reimagining of how marketing executives allocate their scarcest resource: human cognition.

The data backs up what Kyle has experienced firsthand. Research shows that 78% of organizations now use AI in at least one function, yet only 1% describe their AI rollouts as “mature.” More telling: companies that successfully integrate AI into marketing workflows achieve 60% greater revenue growth than peers, but 70-85% of AI projects fail to meet desired ROI. The difference? It’s not the technology. It’s the operating philosophy.

Kyle’s perspective matters because he’s lived through the evolution. At Looker, he witnessed the shift from traditional outbound to problem-oriented positioning. At Clari, he scaled a category design playbook. At Copy.ai, he experimented with AI-native workflows. Now at ClickUp, he’s synthesizing these lessons into what might be the clearest articulation yet of how marketing leadership must evolve—not by doing different work, but by fundamentally restructuring how work gets done.

The AI Resource Paradox: Why Your Team Hasn’t Gotten Smaller

Most revenue leaders assume AI adoption follows a simple equation: automation = fewer people. The research reveals something far more interesting.

Kyle observed what academic studies now confirm: 84% of marketing teams report no decline in team size despite 83% reporting increased productivity from AI. This apparent paradox resolves when you understand what’s actually happening. Teams aren’t shrinking—they’re undergoing what Kyle calls “cognitive reallocation.”

Here’s the pattern Kyle has observed across three platform transformations: organizations that treat AI as a cost-cutting tool achieve marginal gains at best. Those that treat it as a cognition-multiplier achieve what BCG research quantifies as 1.5x higher revenue growth and 60% greater competitive advantage.

The distinction maps to what researchers call the 70-20-10 principle. AI leaders invest 70% of their resources in people and processes, 20% in technology and data, and only 10% in algorithms. Yet most organizations do the inverse—obsessing over which AI tools to buy while neglecting the harder work of fundamentally redesigning how marketers spend their mental energy.

Kyle’s framework for thinking about this centers on a simple question: “What percentage of your team’s cognitive capacity is spent on work that AI could handle versus work that only humans can do?” At most organizations, the answer is uncomfortable. Research suggests 30-50% of marketing time goes to low-value work—content reformatting, meeting scheduling, report generation, data entry.

The teams that win aren’t eliminating this work. They’re codifying it, then offloading it to AI, which frees senior talent to focus on what Kyle calls “the only defensible marketing activities”: understanding customer psychology, designing category narratives, building authentic relationships, and making strategic bets that require human judgment.

Process Documentation as Competitive Moat: The Counter-Intuitive Path to Velocity

Here’s where conventional wisdom gets completely upended. Most marketing leaders view process documentation as bureaucratic overhead—the thing you do when you’re mature and slow. Kyle argues the opposite: rigorous process codification is what enables speed.

This reflects research showing that centralized knowledge bases enhance productivity by up to 30%, and organizations with structured documentation reduce incident resolution times by 30% while experiencing 20% overall productivity boosts. But the real insight Kyle surfaced goes deeper than efficiency metrics.

He references what ClickUp has identified as “work sprawl”—the proliferation of tools, documents, and processes that fragment organizational knowledge. The average marketing team now uses 91 different cloud services. Each tool contains trapped knowledge. Each handoff between systems creates friction. Each context switch burns cognitive capacity.

Kyle’s approach at ClickUp centers on what he calls “process AI-fication”—a three-step framework:

First, document everything. Not in the sense of creating SOPs that sit in dusty folders, but building living, executable process maps that capture decision trees, exception handling, and quality criteria. This feels slow initially. It is slow. But it’s the necessary foundation.

Second, identify the 80/20. Which processes are truly high-leverage and require human judgment? Which are repeatable, rules-based work that could be systematized? Kyle cites Pareto’s principle: 80% of marketing work follows predictable patterns. That 80% becomes your automation target.

Third, build content waterfalls. This is where the magic happens. Kyle described how they transform a single piece of trapped knowledge—say, a sales call recording or a strategy document—into a cascade of formats using AI. The same insights become blog posts, social content, sales enablement materials, customer education, and internal knowledge base articles.

What’s fascinating is that the constraint isn’t AI capability—it’s having clear enough processes that AI knows what to generate. Organizations without documented workflows can’t scale AI effectively because there’s no “recipe” to automate. Those with rigorous processes discover that AI can execute the recipe faster and more consistently than humans ever could.

The result: marketing teams spend less time on production and more time on what Kyle calls “strategic problem-solving”—the work of understanding customer psychology, identifying market shifts, and designing narrative strategies that only humans can do.

Owning the Problem: Category Design in an AI-Saturated Market

Since recording the episode, I’ve already referenced Kyle’s framework for category ownership multiple times. It’s one of the sharpest and most useful insights I’ve heard in a while and his perspective on category design carries weight because he’s executed it at scale. At Clari, he helped establish “Revenue Operations” as a distinct category. At ClickUp, he’s evangelizing “work convergence” as the antidote to “work sprawl”.

The fundamental insight aligns with what Christopher Lockhead and Category Pirates have researched extensively: category kings capture 76% of total market capitalization in their categories, leaving competitors to fight over 24%. But Kyle adds a crucial practitioner insight that academic research misses: problem evangelization has gotten both harder and more important in the AI era.

Here’s why. In a world where AI enables hyper-personalized outreach at scale, every buyer is drowning in solution pitches. LinkedIn is saturated with vendors claiming their product is “better.” But very few companies are prosecuting the problem itself; making the problem bigger than the solution, educating buyers on why existing approaches are fundamentally broken, and owning the language customers use to describe their pain.

Kyle references what he learned from observing Clari’s success: the companies that win categories aren’t necessarily those with the best product; they’re the ones that best articulate the problem. Salesforce didn’t win CRM by building better software than Siebel. They won by prosecuting the problem of “expensive, complex, on-premise software” and creating new language: “No Software.” HubSpot didn’t beat Eloqua feature-for-feature. They evangelized the problem of “interruptive outbound marketing” and created new vocabulary: “Inbound Marketing.”

At ClickUp, Kyle is applying this playbook to what he calls the “work sprawl crisis.” The problem isn’t that existing project management tools are bad, it’s that organizations are drowning in disconnected tools. The average knowledge worker now switches between 10 applications per hour. This fragments context, creates information silos, and makes it impossible to maintain the kind of unified process documentation that enables AI-fication.

The strategic move isn’t positioning ClickUp as “better Asana” or “better Monday.com.” It’s evangelizing convergence as a fundamental paradigm shift and positioning ‘tool proliferation’ as the underlying disease that’s killing organizational productivity.

What makes this approach powerful is what researchers call “cognitive anchoring.” When ClickUp educates buyers on work sprawl first, then introduces convergence as the solution, they’re not competing on feature checklists. They’re competing on problem definition. And the research is clear: the company that best frames the problem often defines and dominates the category.

Kyle emphasized that this requires missionary rather than mercenary thinking. You can’t fake problem evangelization. It requires genuine belief that you’re solving something important, combined with the patience to educate markets before monetizing them. Category design is a 6-10 year play, not a quarterly optimization.

The Change Management Reality: Why 85% of AI Deployments Fail

Here’s where Kyle’s experience becomes particularly valuable. He’s not just theorizing about AI transformation; he’s leading it in real-time across a global marketing organization.

The statistics are sobering: 70-85% of GenAI projects fail to meet ROI expectations. 95% of pilots fail to reach meaningful P&L impact. Yet 78% of organizations are deploying AI anyway, often without clear change management frameworks.

Kyle’s diagnosis aligns with what RAND Corporation research identifies as the root cause: 70% of AI implementation challenges are people and process issues, not technical problems. Organizations obsess over which tools to buy while neglecting the harder work of creating accountability and exposure to new possibilities.

Kyle described ClickUp’s approach using what I’d call the “exposure-accountability flywheel“:

First, create exposure to what’s possible. This isn’t about mandating tools. It’s about showing teams concrete examples of how AI transforms their specific workflows. Kyle emphasized that generic AI training fails because it’s too abstract. What works is demonstrating to a content marketer how AI can transform a single blog post into 15 different formats in 10 minutes, or showing a demand gen lead how AI can analyze 1,000 sales calls to surface the most effective messaging patterns.

Second, establish clear accountability. This is where most organizations fail. They deploy AI tools but don’t update performance metrics, don’t assign ownership, and don’t track outcomes. Kyle’s teams have explicit KPIs around AI utilization—not as surveillance, but as progress tracking. If someone isn’t using AI for appropriate tasks, the question isn’t “why are you being lazy?” but “what’s blocking you from adopting this?”

Third, reward the behavior you want to see. This goes beyond compensation. It’s about public recognition, career advancement tied to innovation adoption, and most importantly, protecting time for learning. Kyle mentioned that they explicitly budget “research spikes”—periods where team members can experiment with new AI capabilities without pressure for immediate ROI.

The research on change management frameworks proves this approach. Organizations following structured methodologies like Kotter’s 8-Step Model or Prosci’s ADKAR framework achieve significantly higher success rates. But Kyle adds a practitioner insight: change management for AI requires different emphasis than traditional technology adoption.

Why? Because AI fundamentally changes the nature of work in ways that previous technologies didn’t. Excel augmented human calculation and AI challenges whether humans should be doing certain cognitive tasks at all. This triggers existential questions about role identity and value contribution that pure process training can’t address.

Kyle emphasized the importance of reframing AI adoption from “your job is being eliminated” to “your job is being elevated.” The marketers who embrace AI aren’t replaced—they’re freed to focus on strategy, creativity, and relationship-building that machines can’t replicate. But this reframing requires ongoing communication, tangible examples, and psychological safety to experiment and fail.

The organizations that get this right achieve what the data shows: 60% higher revenue growth, 50% faster campaign turnarounds, and critically, higher employee satisfaction because people spend time on meaningful work rather than administrative drudgery.

The Blurring Lines: How GTM Functions Are Converging

Kyle’s observation about blurred lines between marketing, sales, and product reflects a broader trend the research confirms: we’re witnessing the death of siloed revenue functions.

The data is striking. Companies with aligned sales and marketing teams see 24% faster revenue growth and 27% faster profit growth over three years. Yet 65% of marketing and sales professionals struggle with leadership misalignment. The gap represents massive competitive advantage for organizations that figure it out.

Kyle described how this plays out practically at ClickUp. Marketing no longer stops at MQL generation. Sales no longer owns the entire customer journey. Product teams aren’t isolated in roadmap planning. Instead, they operate as unified revenue teams with shared metrics around pipeline velocity, conversion rates, and customer lifetime value.

This echoes what the research on Revenue Operations shows. Organizations adopting integrated RevOps models grow revenue 19% faster and are 15% more profitable than peers. But the transition isn’t easy. It requires fundamental restructuring of how work gets done.

Kyle’s framework for thinking about this centers on what he calls “outcome ownership vs. activity ownership.” Traditional structures assign activities: marketing generates leads, sales converts them, customer success retains them, product builds features. Modern structures assign outcomes: the entire GTM team owns revenue growth, retention rates, and customer satisfaction. The question shifts from “did you complete your activity?” to “did we achieve the outcome?”

This requires several structural changes Kyle has implemented:

Shared data platforms. No more separate marketing automation, CRM, and product analytics systems that don’t talk to each other. Unified customer data that everyone accesses and contributes to.

Cross-functional planning cycles. Rather than marketing planning separately from sales, they plan together with product input, aligned around customer segments and revenue goals.

Blended teams. Product marketers who spend time with sales. Sales engineers who contribute to content. Customer success insights feeding product roadmap. The boundaries become porous.

Unified metrics. Everyone measured on contribution to pipeline velocity, not siloed metrics like MQLs or sales activities or feature releases.

What’s fascinating is that AI enables this convergence in ways that weren’t practical before. When marketing can instantly analyze sales call transcripts to optimize messaging, when product can see which features correlate with expansion revenue, when sales can access real-time product usage data—the artificial boundaries between functions dissolve.

Kyle emphasized that this doesn’t mean everyone does everything. Specialization still matters. But the specialization becomes “depth in a domain” rather than “ownership of a silo.” A demand gen specialist still has deep expertise in campaign mechanics, but they’re working toward shared revenue goals alongside sales operations, product marketing, and customer success.

Leadership Philosophy: Running Toward the Fire

Kyle’s approach to leadership carries lessons beyond marketing specifically. He described his philosophy as “pushing people and running toward fires“—which initially sounds like typical executive platitudes but reveals deeper wisdom on examination.

The research on high-performing teams shows that psychological safety combined with high standards creates optimal performance. But Kyle adds a crucial nuance: in periods of rapid transformation, the role of leadership isn’t to shield teams from change; it’s to accelerate their ability to navigate it.

“Pushing people” in Kyle’s framework doesn’t mean unrealistic demands or burnout culture. It means holding high standards while providing the support to meet them. It means expecting AI adoption, rigorous process documentation, and category-level thinking then removing obstacles that prevent teams from executing.

“Running toward fires” means the opposite of what most executives do when crises emerge. Rather than delegating problems downward, Kyle described actively engaging with the hardest challenges the organization faces. More than heroic individual contributions, it’s modeling the behavior of confronting difficulty rather than avoiding it.

This connects to what researchers call “transformational leadership”—the kind that inspires teams to exceed their own expectations. But Kyle grounds it in practical behaviors: being first to experiment with new AI tools, personally testing process improvements before rolling them out, transparently sharing what’s not working alongside wins.

The outcome Kyle described aligns with the change management research: teams that see leadership actively engaging with transformation (rather than mandating it from a distance) adopt changes 2x faster and with less resistance.

The Content Waterfall: From Trapped Knowledge to Scalable Assets

Kyle’s most tactical insight centers on what he calls “content waterfalls”—a systematic approach to transforming trapped knowledge into scalable marketing assets using AI.

Here’s the problem most organizations face: valuable insights exist in sales call recordings, strategy documents, customer conversations, and team members’ heads. But extracting and scaling this knowledge requires manual effort that doesn’t happen consistently. The result: knowledge remains trapped, underutilized, and eventually lost.

Kyle’s content waterfall framework has three layers:

Layer 1: Capture. Record everything—sales calls, strategy sessions, customer interviews, brainstorming meetings. The key is making capture automatic and low-friction. Tools like Grain, Otter, or Fathom handle transcription and key moment extraction.

Layer 2: Extract. Use AI to identify core insights, frameworks, and narratives from raw transcripts. This isn’t just summarization—it’s pattern recognition across multiple conversations to surface themes, objections, messaging that resonates, and competitive intelligence.

Layer 3: Generate. Transform extracted insights into multiple formats—blog posts, social content, sales enablement scripts, customer education materials, internal knowledge base articles, presentation decks. The same insight becomes 10-15 different assets optimized for specific channels and audiences.

What makes this powerful is what Kyle calls “insight leverage“—getting maximum value from each piece of captured knowledge. A single sales call with a customer describing their “work sprawl” problem might become:

• A LinkedIn post about the hidden costs of tool proliferation

• A blog post analyzing the productivity impact of context switching

• A sales email using the customer’s exact language to describe the problem

• A case study (with permission) showing the challenge and solution

• An internal knowledge base entry for how to handle this objection

• A slide in the next board presentation about market insights

The research backs this up. Organizations with robust knowledge management practices see 20-30% productivity improvements. But Kyle’s insight goes beyond efficiency: content waterfalls enable consistent problem evangelization at scale.

When you’re systematically capturing how customers describe their problems and using AI to generate problem-oriented content across all channels, you’re not just creating marketing materials—you’re establishing the language and narrative of the category itself.

The Mandate Hasn’t Changed—The Operating Model Must

Kyle’s ultimate insight returns to where we started: the fundamental mandate for marketing leadership remains exactly the same—drive revenue growth through customer acquisition and expansion—but the resource equation that enables this has been completely transformed.

The CMOs and VPs of Marketing who will succeed in the next decade aren’t those with the biggest teams or budgets. They’re those who master the art of cognitive reallocation—freeing human talent from automatable work to focus on the irreducibly human tasks of understanding psychology, designing narratives, building relationships, and making strategic bets.

This requires several simultaneous transformations:

Operational: Rigorous process documentation and AI-fication of repeatable work
Strategic: Shifting from solution marketing to problem evangelization and category design
Organizational: Breaking down silos between marketing, sales, and product into unified revenue teams
Cultural: Leading change management that creates exposure and accountability for AI adoption
Personal: Modeling the behavior of running toward complexity rather than away from it

The data shows the stakes clearly. Organizations that execute these transformations achieve 60% higher revenue growth, 2x faster market adaptation, and sustainable competitive advantage. Those that don’t face the prospect of fighting for the remaining 24% of market share while AI-enabled competitors capture the lion’s share.

Kyle Coleman’s journey from SDR to marketing leader at some of B2B SaaS’s most successful companies has given him pattern recognition most marketers lack. He’s seen what works when companies transform from single products to platforms, from reactive to category-defining, from siloed to integrated.

The lesson isn’t that every organization should copy ClickUp’s or Clari’s playbook exactly. It’s that the fundamental operating model for marketing leadership must evolve from managing headcount and budgets to orchestrating the optimal balance of human cognition and AI capability.

In Kyle’s words: “The mandate is the same. The resources are different. And if you keep trying to execute a 2020 playbook with 2025 capabilities, you’re not going to survive.”

The future belongs to revenue leaders who recognize that the scarcest resource isn’t budget or tooling—it’s human attention and strategic judgment. The organizations that win will be those that ruthlessly protect this scarce resource by offloading everything else to the machines designed to handle it.

That’s not a technology transformation. That’s a leadership transformation.

And the clock is already running.