AI Discipline Was the First Conversation

Revenue Growth Is the One That Matters Now

I've already made the case for why AI discipline has to come first. If your data is fragmented, if marketing and sales aren't aligned, if nobody can sit in an ROI conversation comfortably, AI doesn't fix that. It accelerates it. You can read that argument in full [here].

That work matters. But discipline is not a revenue strategy. It's the foundation you build one on.

This piece is about what comes next. Specifically, how AI becomes a driver of bottom line growth not just a cleaner way to run operations. That's the conversation most organizations are behind on, and the gap is starting to cost them.

Most Companies Are Still Playing Defense

Efficiency was the logical entry point for AI. It's where executive comfort lives. Automate repetitive tasks, produce content faster, tighten media spend. McKinsey's 2025 State of AI report confirms it — 80% of companies set efficiency as their primary AI objective.

That's understandable. Margin protection matters.

But here's what the same research makes clear: the companies actually generating the most value from AI are the ones that set growth or innovation as additional objectives and not just efficiency. Efficiency stabilizes performance. It does not expand revenue. Those are two different jobs, and right now most organizations are only asking AI to do one of them.

Faster signal to action. Shorter distance between insight and revenue.

The shift that actually moves the growth number happens when AI changes how fast you can detect demand, respond to it, and convert it. Faster signal to action. Shorter distance between insight and revenue. That's the use case most organizations haven't gotten to yet and it's the one that separates companies that are using AI from companies that are growing because of it.

If your AI initiatives are still primarily about producing content faster or reducing headcount, you're playing defense. The next question to put in front of your team is simple: what revenue motion is too slow right now, and could AI compress that timeline?

The Competitive Gap Is Quiet. That's What Makes It Dangerous.

I want to be direct about what I'm actually seeing, because it's not the dramatic disruption story that gets clicks.

Most companies are not being suddenly overtaken. Nobody's waking up to find their market gone. What's happening is quieter and in some ways harder to defend against precisely because it doesn't trigger alarms.

A subset of organizations is simply moving faster:

• They're running more experiments per quarter.

• They're adjusting targeting and creative in near real time rather than waiting for a monthly report.

• They're reallocating budget based on live performance signals rather than quarterly reviews.

• The gap between when they sense something and when they act on it is shrinking.

McKinsey's data puts real weight behind this: companies using AI to drive growth are nearly three times as likely as their peers to have fundamentally redesigned their workflows and that intentional redesign is one of the strongest predictors of meaningful business impact. This isn't a marginal difference in execution style.

It's a structural advantage that compounds every quarter:

• Campaigns gain traction earlier.

• Pipelines build more predictably.

• Sales cycles tighten.

None of it looks dramatic in isolation. Over 24 to 36 months, it becomes very expensive to close.

The risk isn't collapse. It's the kind of slow revenue divergence that's already happening before it shows in your numbers.

My recommendation here: 

Do an honest audit of your decision-making lag. How long does it actually take from a performance signal to a budget or creative decision? If the answer is weeks, that's the first gap worth closing. You don't need a large technology investment to fix it, you need clearer internal decision rules and someone accountable for acting on live data.

The Revenue You Don't See Leaving Is the Most Dangerous Kind

I've written before about what AI search does to your measurement. What I want to focus on here is what it does to your revenue because those are two different problems and the second one is more urgent.

According to IAB research, among people who use AI for shopping, AI is now the second most influential source behind only search engines surpassing retailer websites and even recommendations from friends and family. Nearly 90% say AI helped them discover products they wouldn't have found otherwise.

Sit with that for a moment. Word of mouth, historically your highest-converting referral source, is now losing ground to AI-generated recommendations. And most brands have no deliberate strategy for showing up in those moments.

McKinsey puts a revenue number on what's at stake:

By 2028, $750 billion in US revenue will funnel through AI-powered search. Brands that aren't prepared could see a 20 to 50% decline in traffic from traditional search channels.

Right now, only 16% of brands are systematically tracking their AI search performance.

That 16% number is the one that should concern you most. Because the revenue impact of being invisible in AI-mediated discovery doesn't announce itself. There's no bad quarter you can point to. No single moment where it breaks. Instead, share of consideration narrows quietly. Pipeline quality softens. Win rates drift. By the time it registers in your financial results, a competitor has been capturing that demand for 18 months and they have a head start you'll have to pay to overcome.

This is not a traffic problem. It's a revenue timing problem. And revenue timing, compounded over years, is exactly how competitive gaps become permanent.

My recommendation here: 

Audit how your brand shows up in AI-generated responses today. Search your core service categories in ChatGPT, Perplexity, and Google's AI Overviews.

• Are you surfaced?

• How are you described?

• Is the information accurate and compelling?

If you can't answer those questions, that's your starting point. AI visibility doesn't happen by accident, it requires structured content, consistent authority signals, and deliberate optimization. At this point, that's not optional infrastructure. It's revenue infrastructure.

Where Actual Revenue Growth Shows Up

When AI is connected to a specific revenue constraint and not deployed generically across the marketing function, the impact becomes tangible and measurable.

Better alignment between intent, message, and offer drives conversion improvement. Small percentage gains at scale translate directly to topline revenue. AI-assisted scoring and sales enablement reduces friction between marketing and sales, tightening win rates and making forecasting something closer to science than guesswork. And AI dramatically lowers the cost of testing new segments, verticals, or geographies so you can validate growth hypotheses faster and allocate capital with more confidence.

The common thread across all of it is intentionality. AI tied to a defined revenue constraint produces results. AI deployed as a general upgrade produces activity.

My recommendation here: 

Pick one. Not three initiatives, not a cross-functional working group. One specific revenue constraint:

• Conversion rate

• Sales cycle length

• Cost of entering a new market

and build a focused AI application around it. Prove the impact, document it clearly, then expand. Organizations that try to do everything at once end up with dashboards full of AI activity and nothing they can take to a board.

The C-Suite Is Running Out of Patience

Reporting on AI pilots and tool adoption used to buy goodwill in the boardroom. That window is closing faster than most marketing leaders realize.

McKinsey's CxO survey data exposes the tension clearly: only 19% of executives report AI-driven revenue increases above 5% today — but 87% expect revenue growth from AI within the next three years.

That gap between current reality and executive expectation is exactly where marketing leaders are being asked to perform right now. And the ask is getting more specific.

It's no longer enough to report that AI is being used. Executives want to know where specifically it's expected to accelerate revenue. They want evidence that learning cycles are actually getting shorter. And they want to see financial performance, not activity metrics.

The organizations that close that gap will not be the ones that adopted the most tools. They'll be the ones that connected AI to a revenue hypothesis and measured it with discipline.

My recommendation here: 

If you're presenting AI strategy to leadership, lead with a revenue hypothesis not a capability list. We are using AI to compress our sales cycle by identifying higher-intent accounts earlier in the funnel, and here is how we are measuring it lands differently than we have implemented several AI tools across the marketing function. One is a growth strategy. The other is a budget line item.

The Second Phase Is About Advantage, Not Readiness

The first phase of AI was about getting ready. Most organizations are still somewhere in that phase, and that's fine, but readiness is a starting point, not a competitive position.

The second phase is about revenue advantage. It comes from connecting AI to growth velocity in ways that are measurable, repeatable, and tied explicitly to the bottom line.

The companies that separate themselves over the next few years will not be the loudest about AI. They will be the ones who identified a specific revenue constraint, built a disciplined approach around it, proved it, and kept tightening it quarter over quarter while everyone else was still running pilots.

AI can remain an operational upgrade. Or it can become the reason your revenue grows faster than your competitors'.

That is a leadership decision. Not a technology decision.

To Close

If your team is experimenting with AI but struggling to draw a clear line to revenue outcomes, you are not behind you are in the majority. Most organizations are in exactly that position right now.

The question that tends to unlock it is not where can we use AI? It's where is revenue constrained and how does AI remove that constraint?

That shift in framing changes everything about where you focus, what you measure, and what you can prove.

And proving it is the whole point.

Sources:

• McKinsey

• IAB / Talk Shoppe