The hardest part of building AI products isn’t the model. It’s deciding which workflow you’re willing to own.

That insight comes from Aatish Nayak (VP of Product at Harvey) and Sachi Shah (Product Manager at Sierra), speaking on Kleiner Perkins’ Builders series. Both are building AI that doesn’t assist workflows—it runs them.

We’ve been thinking about the same thing. Not for legal. Not for customer experience. For marketing.

The Workflow Ownership Thesis

Harvey started by owning a single legal workflow: transactional corporate work. High volume, repetitive, contained data. Then they expanded—from Q&A on a few documents to analyzing thousands, enabling collaborative AI tools across entire legal teams.

Sierra took a similar path in customer experience. They didn’t build a chatbot. They built the infrastructure for CX teams to deploy autonomous agents, measured by customer outcomes like “time to music” for Sonos—not tickets closed.

The pattern is clear: own the end-to-end workflow, not a feature within someone else’s.

Both companies report the same discovery: once you give users workflow-level AI, they find use cases you never anticipated. Harvey’s customers went from legal review to investor presentations. Sierra’s agents went from support tickets to proactive customer engagement.

As Nayak puts it: “Traditional product management says, pick a user, pick their use case, make it super narrow. With AI, you can actually broaden out.”

What This Means for Marketing

Most marketing AI tools are features. Generate a caption. Write an email. Analyze a dashboard. They sit inside someone else’s workflow—your workflow—and make one step faster.

That’s theme one from a16z’s framework: traditional software going AI-native. Useful, but not transformative.

The workflow ownership thesis says something different: the AI that owns discovery, planning, and execution across the entire marketing function will win.

Not a writing tool. Not a scheduling tool. Not an analytics dashboard. A system that:

Discovers which channels work for your brand
Creates content that sounds like your team wrote it
Executes across channels autonomously
Learns from results and adjusts
Scales without headcount

That’s the workflow Lane owns.

Re-Earning Attention in Real Time

One of the most striking quotes from the conversation: “You have to constantly re-earn product-market fit. Customer expectations change.”

The same is true for brand attention. You don’t earn mindshare once—you re-earn it every day, in every channel, in every conversation that matters.

Here’s where this gets interesting.

This very blog post is a reaction to a video published two days ago. We watched the conversation, identified the marketing implications, and published our take. That’s a manual process today. It took human judgment, writing, and editorial decisions.

But what if it didn’t have to be?

The Real-Time Brand Commentary Thesis

Imagine an AI CMO that doesn’t just execute scheduled campaigns—it watches.

It monitors the conversations that matter to your brand: industry podcasts, influencer posts, conference talks, competitor announcements, regulatory changes. When Aatish Nayak talks about workflow ownership on a Kleiner Perkins video, your AI CMO recognizes the relevance, drafts a reaction that positions your brand in the conversation, and publishes—with your approval or autonomously.

Not spam. Not generic comments. Substantive, brand-aligned commentary that introduces how your product relates to what was just discussed.

This is the difference between broadcasting and participating.

Traditional Marketing AI	Real-Time AI CMO
Schedules posts on a calendar	Joins conversations as they happen
Creates content from prompts	Creates content from context
Reacts to your instructions	Reacts to the market
Operates in your channels	Operates in your audience’s attention

Sachi Shah’s framing applies perfectly here: you need an “agent development life cycle” for marketing—where AI agents are built, tested, deployed, and improved based on real outcomes. Not just content generation, but context-aware brand participation.

The Error Budget Principle

Shah makes another point that resonates: “SRE teams work with error budgets because we’ve understood that 100% reliability is not really a goal. The same is true with agents.”

This is critical for marketing AI. The reason most companies don’t let AI post autonomously isn’t quality—it’s fear of imperfection. But the cost of silence is higher than the cost of an occasional B+ post.

A human CMO doesn’t get every post perfect either. They operate with an implicit error budget. The question isn’t “will AI be perfect?”—it’s “will AI be better than doing nothing in 15 out of 19 channels?”

For most businesses, the answer is obviously yes.

The Forward-Deployed CMO

Both Harvey and Sierra emphasize the role of forward-deployed engineers—experts embedded with customers who deeply understand their workflows and feed insights back to the product.

Lane takes this concept and makes it the product itself. Lane is a forward-deployed CMO—embedded in your brand, understanding your voice, your audience, your competitive landscape. Every interaction generates data that makes the next campaign more effective.

The difference: Harvey’s FDEs are human. Lane’s forward-deployed marketing intelligence is AI—which means it scales to every customer without marginal cost.

What This Means for 2026

Workflow ownership beats feature building. Marketing AI that owns the full loop—discover, create, distribute, measure, optimize—will outcompete point solutions. The marketing team of the future is a human director and an AI operator.
Real-time reaction is the next traction channel. Brands that join conversations as they happen—not days later—will capture disproportionate attention. This isn’t newsjacking. It’s systematic, brand-aligned participation powered by AI that understands context.
Error budgets unlock autonomy. The companies that ship AI marketing at 90% quality today will compound advantages over those waiting for 100%. Perfect is the enemy of present.
Your brand data is your moat. Just as Harvey’s legal data and Sierra’s CX data make their products more defensible, your brand DNA—voice, audience response patterns, channel performance—becomes a moat that no competitor can copy.
Broadening beats narrowing. The old playbook says find one channel and double down. The AI playbook says test all 19 and let data decide. Lane exists because the cost of testing dropped to near zero.

Lane is building the AI CMO that doesn’t just execute your marketing—it joins the conversations that matter to your brand. Start free →

References

Aatish Nayak (Harvey), Sachi Shah (Sierra) — “What Product Leaders Must Rethink in the AI Era” (Kleiner Perkins Builders, Feb 2026)
Harvey — AI-powered legal workflow platform
Sierra — AI-powered customer experience platform
Related: Software Is Eating Marketing Labor — Our response to a16z’s AI opportunity thesis
Related: The 19 Traction Channels Explained — Why most businesses use 3-4 channels when 19 exist