As you look at how your company is working with AI and automation, you might be asking yourself:
- We're investing heavily, but is it actually saving us time and money?
- My teams are moving faster, but are we just accelerating a broken process?
- We blast hundreds of outbound messages...but do I cringe when I read them? Are they reaching the right people?
- We haven't had success in using AI to aid the design process. What are we missing?
If any of that feels uncomfortably familiar, you’re not alone.
Across product, design, sales, and marketing, AI and automation is here. Tools are improving quickly and budgets are shifting aggressively to take advantage of them. At a recent Growth Factors conference for private-equity backed companies, one data point suggested roughly two-thirds of new budget is flowing into AI-related tools and software.
The question isn’t whether you’ll use AI-driven automation. You already are.
The real question is: are you investing in the fundamentals that enable AI tools and processes to be effective and get real results?
More and faster is not the goal. Accelerating effective outcomes is.
Automation is an Accelerator, Not a Strategy
AI tools promise speed:
- Faster product design and prototyping
- Faster UX exploration
- Faster prospect research
- Outbound outreach at scale
- Faster content creation
Speed is attractive—especially when your value creation plan is under a microscope. But here’s the catch: automation only accelerates what you feed it.
If your workflows are unclear, your user experience is outdated, and your messaging is fuzzy, AI doesn’t fix that. It just scales what you already have. You get more output and you get it faster, but the results can be ineffective and lacking in quality.
Katie Lukes, our VP of Product Strategy & Research, used this analogy, “You wouldn’t hire 100 junior designers and turn them loose without a design system. You wouldn’t hire a bigger sales team without a clear pitch, target ICP, and enablement.”
AI is the same. It’s leverage. Not leadership.
Product Experience: AI Can’t Fix an Outdated UX—It Only Exposes It
On the product side, teams are experimenting with AI-assisted prototyping, UX design automations, and tools that can generate interfaces or code from a single prompt. Tools like Figma Make, Lovable, Claude, and Cursor can produce screens instantly, while Granola and Notion AI help teams move faster on requirements, research, and early ideation.
These tools can absolutely help. But AI can’t magically infer your user workflows, interaction patterns, layout rules, component library, or accessibility standards. It only works correctly when fed a modern, well-structured design system foundation.
A CTO we work with recently told us, “Every AI design tool I use has a prerequisite: an effective design system. If I don’t have that, I can’t use these tools to generate anything I’d actually ship.”
The fundamentals that matter most for product automation:
- User personas—Clear understanding of who your users are, what they need, and how they work.
- Product vision and workflows—Aligned understanding of what the product does, why it exists, and how users move through it.
- A strong design system—Components, patterns, spacing, typography, interactions, and rules that define "how we design here."
- Information architecture—A structure that makes sense to users and that tools can leverage to place functionality properly.
Once those fundamentals are in place, AI tools become a true accelerator:
- Designers can explore more options in less time—without breaking consistency.
- Product teams can valdidate flows faster because the building blocks are stable.
- Engineers can trust that what comes out of AI fits your system, not just looks good in isolation.
Without those fundamentals, AI can still “spit out screens.” But they’re not screens your users will actually love, or your team can maintain.
Go-to-Market: Automation Is Powerful—But Only If Your Story Is Clear
On the go-to-market side, we’ve seen how RevOps automation is evolving quickly.
Teams are using tools like Clay that scour the market for prospects based on live signals, not just static firmographics, and assemble lists based on hiring changes, product launches, or digital activity. Paired with tools like Twain which can generate and send outbound messages at scale, teams suddenly can automate large portions of research and outreach.
RevOps experts describe this approach as “GTM engineering,” or encoding your go-to-market strategy into AI tools so they can act on your behalf. But that process only works if the inputs are strong. And that depends entirely on having a strong product marketing framework in place first.
Tools like that promise to eliminate hours of manual research and outreach. But again, they only work if your fundamentals are clear.
If you don’t know who you’re targeting, what their pain points are, what signals matter, or what to say to them, then even the smartest RevOps stack can’t help you. It sends the wrong messages to the wrong people—faster.
The fundamentals that matter most for GTM automation:
- Ideal customer profile (ICP)—Defined not only by title and company size, but by pain, context, and buying triggers.
- Signals and events—Clear, prioritized indicators that a company is likely a strong fit for you.
- Messaging framework—For each persona, their pains, outcomes they care about, and the language that resonates.
- Positioning and story—A simple, differentiated explanation of who you serve, what you offer, and why it matters.
- Brand voice and clarity—The tone, cadence, and vocabulary that ensure your automated outreach still sounds like you.
When you have these fundamentals established, RevOps tools can:
- Identify and search for the right signals.
- Construct lists that actually match your ICP.
- Generate outreach that sounds like your brand and speaks to real problems.
- Maintain brand consistency across every touchpoint.
When you don’t, you end up with emails that make you cringe, campaigns that miss your best-fit buyers, and activity that looks impressive on a dashboard, but doesn’t deliver impactful results.
More outbound is not the goal. More of the right conversations with the right prospects is.
Three Questions You Should Ask Before Doubling Down on AI
If you’re leading a product, design, or go-to-market at a high-growth company, odds are you’re already investing in AI—through tools, tokens, platforms, and pilots.
Before you increase your budget, ask yourself:
Are we clear on the fundamentals we’re trying to scale? Do we have up-to-date UX standards, design systems, and product workflows? Do we have personas, ICPs, and messaging we trust?
Is AI accelerating outcomes—or just activity? Are we seeing better product adoption, stronger pipelines, faster sales cycles, or just more “stuff” getting produced?
Would we be comfortable scaling this manually? If 100 designers or 100 sales reps behaved exactly as our tools do today, would we be proud of the work?
If the honest answer to any of those is “not yet,” your next investment probably shouldn’t be another AI tool.
It should be the strategy and fundamentals that anchor everything.
Faster Isn’t the Goal. Better Outcomes Are.
For high-growth tech companies, the stakes are high. With tight timelines, shrinking budgets, and competing priorities across product, sales, marketing, and operations, teams need to use every advantage.
It’s tempting to believe automation alone will help you “do more with less.” But the leaders who will win in the long run won’t be the ones who adopt AI the fastest. The old adage to “measure before you cut” applies here: Invest in the fundamentals first, and it will pay dividends in making your automation effective.
Get the foundation right—your product experience, your go-to-market strategy, your brand and messaging. Then, let automation do what it does best.
Not simply make you faster, but help you reach the right outcomes, sooner.
