CMOs are putting 15%+ of their budgets into AI, but most marketing pilots never reach production. Here’s why they stall and how the right approach to AI development helps GTM plans reflect the market right now.
How many of the assumptions in your current go-to-market plan are still true? Lots of marketing teams build the plan once a year, check it against results each quarter, and adjust in between as well as they can. By the time a quarterly review shows that a channel or initiative is underperforming, a good share of the budget has already been spent against the wrong opportunities. Many marketers also say that preventive or optimizing actions could have been taken if integrated data had been available much earlier.
Marketing leaders see this problem, and they’re putting money toward it. Gartner’s 2026 CMO Spend Survey of 401 marketing leaders found that CMOs now allocate an average of 15.3% of their budgets to AI. The same survey found that only 30% of marketing organizations have mature AI readiness, and 70% of CMOs say their internal processes aren’t mature enough to implement and scale AI. Most teams are funding AI before they’ve set up the processes to get value from it. Often, marketing leaders are even unaware of what they are funding when teams make purchases on their own, without oversight. They need a holistic view of how to build an integrated foundation that can scale.
Where plans break down
A GTM plan rests on a small set of big judgments: which customers to pursue, what to offer them, which channels to use, and how much to spend reaching them. Each of those judgments depends on data owned by separate teams. Sales ops owns the CRM, campaign results live in the ad platforms, and finance holds pricing and margin, to name just a few.
When a marketer wants to know whether a new segment is worth the investment, someone has to pull those sources together, reconcile them, and build the analysis. That can take days or even weeks, so teams often settle for a proxy such as last year’s conversion rates or the segment definitions left over from the previous planning cycle. Worse yet, wild assumptions may be ‘copy/pasted’ from one audience segment to another without validation. The plan then inherits whatever was wrong with the proxy.
What if segments could keep up...
Traditional segments are built on firmographics or demographics and refreshed periodically. Machine learning models can segment on behavior, including what customers buy together, how often they engage, and which signals came before churn or expansion in the past. Those models retrain as new data arrives, so a campaign targets customers based on how they’re acting now.
AutoFi, a RapidCanvas customer in automotive financing, used this approach to build a dealer engagement model with a complete view of dealer behavior data. The company cut dealer churn by 15%, lifted estimated dealer satisfaction by 24%, and saved $1.5 million in revenue.
...budget could follow results...
Marketing mix models and multi-touch attribution have been around for decades. For most companies, they’ve been expensive annual exercises, and the recommendations arrive after most of the money is committed. AI models can run continuously, estimate the marginal return of each channel as results come in, and recommend adjustments while the plan can still change.
If paid social’s cost per qualified lead climbs for three weeks in one region while partner referrals hold steady, the model flags the change and recommends moving budget before the quarter closes. The marketer still makes the final call, only with current numbers in hand.
...and marketing and sales worked from the same data?
Plenty of GTM plans fail at the handoff between marketing and sales, when marketing counts a lead as qualified and sales disagrees. AI gives both teams a common basis for decisions across the funnel, including:
- Lead scoring trained on the company’s own closed-won and closed-lost deals
- Cross-sell and next-best-offer recommendations drawn from market basket analysis
- Revenue forecasts that update as the pipeline changes
- Churn risk alerts that route at-risk accounts to the right account manager
- Pricing and promotion analysis broken out by segment
- Which clients show outstanding results, providing customer proof and suggesting new sales opportunities
McKinsey’s State of AI research identifies marketing and sales as two of the functions where companies most commonly report revenue gains from AI.
Why marketing pilots stall
Getting AI initiatives into production is harder. I’ve seen many marketing teams buy an AI tool after an impressive demo built on limited, clean data. They then watch it fall short in production. The issues often come down to the same six things:
- Teams have not agreed on context. Sales, marketing, and other teams use different definitions for items like MQL, SQL, and even Closed Won, so the system isn’t trusted or sustained.
- The model doesn’t know the business. AI can’t access all the relevant data, or receives data in a form that makes it difficult or impossible to use constructively. Signals get missed, and misguided decisions get repeated.
- Tools don’t fit the stack. Point solutions that don’t connect to the existing CRM and marketing systems add manual work, and adoption fades once the pilot team moves on.
- Change management is overlooked or underfunded. A huge issue. Even the best AI solution can’t help if no one uses it. Teams need training and transparency on the purpose of an initiative, and the freedom to adapt processes to enhance effectiveness. BCG recently recommended allocating 70% of investment to change management.
- Teams don’t have the people to run it. The Gartner survey found 54% of CMOs report inadequate resources. Many also report that they have data that could be helpful, but don’t have time or people to review, digest, and use it. Limited training and solutions that need constant attention from data scientists further widen the gap.
- Pilots have no route to production. A single-campaign test gets no owner or budget line, and engagement isn’t connected to pipeline and revenue. Without a revenue case, finance can’t fund the rollout.
Scaling AI requires solutions that reflect how the business operates and a team that’s ready and willing to use them.
How RapidCanvas can help
At RapidCanvas, we use a Hybrid Approach™ for solution development, combining human experts with a proven agentic AI platform to deliver a solution that’s customized to your goals, workflows, and tech stack. Our Enterprise Context Engine™ captures the definitions, rules, and priorities specific to your business, so segmentation, scoring, and forecasting models work from the same understanding your team uses. The solutions connect to your existing CRM and marketing tools and make your best people’s expertise available across the organization.
AI takes over much of the weekly manual work, which frees marketers for the strategic work that drew them to the field. Because we build in natural language controls, your team doesn’t need data science expertise to use the solutions. With Compounding Intelligence, each new use case builds on what the system has already learned.
My team and I use these solutions every day in our own marketing. When segment, spend, and forecast decisions can be revised in weeks, less of the budget goes to assumptions that stopped being (or were never) true, and the improvement shows up in pipeline and revenue.
If you’d like to discuss how AI can improve your marketing effectiveness and GTM results with one of our experts, RapidCanvas would love to help. Contact us to schedule a time. You can also visit our website to browse dozens of case studies and read verified customer reviews on G2.
