Hello Reader
There’s a dynamic I hear from marketing leaders right now, and it usually sounds like this.
“My CEO keeps pushing us to drive results with AI, but when I put together a proposal, with budget, headcount, and a timeline, I struggle to get it approved.”
If that feels familiar, you’re not alone, and the problem isn’t your proposal. It’s that most AI programs get stuck in what I call pilot purgatory: lots of small experiments, zero coordinated investment, and a CEO who’s enthusiastic about AI in principle but reluctant to burn cash on unproven efforts.
This week I want to break down why that gap exists, and what you can do to close it.
The Commitment Gap
Global surveys show that most CEOs believe AI is critical to competitiveness and plan to increase investment over the next two years. That sounds promising, but there’s a catch.
Only a quarter of AI initiatives have delivered expected ROI, and only a small minority have scaled enterprise-wide.
The mandate to “use AI” is real, but the willingness to fund a structured program isn’t automatic. Which means that even if your team is ready to move forward, securing executive commitment means identifying the potential land mines and having a strategic plan to address them.
Why Buy-In Breaks Down
When I talk to marketers struggling to get executive support, the blockers tend to cluster around three things.
#1 You’re Missing the Value
If you haven’t connected your AI program to a specific business outcome (like pipeline, cost reduction, or time-to-market) it will always feel like a “nice to have” experiment rather than a strategic investment. Executives fund programs with clear metrics, and a direct impact on revenue.
A recent RevOps survey from tech platform Default proves this case. The data shows that organizations moving beyond surface-level use cases like lead research are seeing stronger results. Teams applying AI to pipeline management, lead routing, forecasting, and qualification report stronger business outcomes.
#2 Fragmented Tech
Around half of CEOs say the pace of AI investment has left them with disconnected, piecemeal systems. When a CEO looks across the organization and sees tools everywhere but outcomes nowhere, their instinct is to pump the brakes.
CIO contributor Daniel Avancini says “fragmented stacks, hand‑coded ETL and static dashboards are dead,” precisely because they produce stale, siloed views and delay decision cycles.
Fragmented data makes AI results difficult to trust. Unified data layers that support decision making give executives confidence that AI investments will produce measurable outcomes.
#3 Skills and Change Gap
Even a CEO who wants to move fast will hesitate when they can’t see a credible adoption path. If your proposal doesn’t address who will run your AI program, how you’ll train your team on the new workflow and tools, or what happens when it doesn’t work immediately, your proposal has too many unknown risks.
"Set up a defined cadence for people to share wins and failures,” Karrie Sanderson, Head of Marketing Strategy at LiveRamp recommends. “And set aside time for people to experiment together."
Many organizations already have a graveyard of AI pilots that never made it past experimentation. CEOs remember those efforts. When they hear another proposal, they’re going to ask you if this will become another failed experiment, or change how the team will work.
How You Get the Yes
Securing approval usually comes down to how the proposal is framed.
Start with the CEO’s goals, not yours
Before building your case, ask what success looks like to them this year, then work backwards. Every element of your AI program should map to that answer.
If the CEO cares about pipeline velocity, show how your AI-assisted content workflow reduces time from brief to publish. If they care about headcount efficiency, show the before-and-after on hours spent per campaign cycle.
As Sanderson recommends, "Focus on the most important workflows both within marketing and across to other teams. Show how you can really move the needle—that will get the flywheel going.’
Show leading indicators
One of the most common mistakes is waiting for closed-won revenue to prove marketing’s value. By then, the budget decisions are made.
Report on early signals like:
- ICP account engagement: Track visits, repeat sessions and high-intent content consumption from target accounts.
- Demo request quality: Measure the percentage of demos that convert to qualified opportunities.
- Sales cycle velocity: Compare average days from first meeting to opportunity creation before and after AI-assisted workflows.
Ted Sapountzis, CMO at Flow Meridian, describes winning approval by quantifying funnel leakage and wasted execution cycles:
“What makes it harder this time around is the AI hype; everyone sees it as a silver bullet, so slowing down to fix foundations feels like you're resisting progress. On the other hand, AI accelerates both success and dysfunction 100x, so the business case actually gets stronger.”
Set clear decision criteria
Executives buy clarity and control. Define the criteria leadership will use to judge success before the program begins, including operational impact such as time saved, revenue signals like qualified pipeline growth, and financial efficiency such as cost per campaign or opportunity.
And if you can’t get approval for a full program upfront, propose a time-boxed pilot with explicit success metrics and a pre-agreed decision gate.
“We’ll run this for eight weeks. If we hit X, we expand. If we don’t, we stop.” Once the CEO sees the success metrics, the case for expansion becomes much easier.
Translate into financial language.
Executives evaluate investments through a financial impact lens. Impressions and engagement rates don’t influence decisions but CAC, LTV, payback period, and contribution margin do.
Charlie Treadwell, CMO at Elisity argues that framing the trade offs between short and long term investments makes leadership decisions easier. “At a recent offsite I explained that we could spend $500 on Google Ads and get clicks that evaporate or I could spend it on Claude API costs and build autonomous agents that compound month over month. The ad spend is rent. The agent infrastructure is equity.”
Win over internal influencers.
When direct persuasion stalls, find the internal influencers (the CFO, the head of sales, the COO) and get them on side first. If sales leadership is vouching for marketing’s impact on pipeline, the CEO is far more likely to fund the program.
“IT and Data Security teams are your new best friends,” says Sanderson. “For marketing AI-driven workflows to be effective, you need to connect to all the data, both structured and unstructured. That means your traditional tech stack, but also customer transcripts from sales calls, and digital storage where your brand guardrails and marketing plans live.”
The marketers who are best at this treat internal selling and executive education as a core skill. They show up to every review cycle with data. They speak the language of the business, and they understand that trust is the actual currency in this conversation, not slide decks.
As HBS Professor Iavor Bojinov put it: before investing in large-scale systems, demonstrate that AI can address genuine problems and deliver meaningful results. “That’s not a barrier to your program. That is the program.”
Your AI initiative won’t be funded on enthusiasm alone, but if you can show your CEO a clear problem, a credible path, and a way to measure progress, that’s when the conversation changes.