CMOs need to test existing commitments before setting 2027 budgets

More money can make a weak marketing plan harder to challenge. For CMOs preparing 2027 budgets, the useful question is which existing assumptions still deserve funding before more money is allocated.

More budget can preserve the wrong marketing system

The conventional budgeting question is where the next dollar should go. A prior question is which existing activities still deserve resources. If leadership skips that review, a larger budget can expand the current portfolio without testing whether its rationale still holds. Investment growth and planning quality are separate issues.

An established program enters a budget cycle with historical metrics, assigned staff, vendor relationships, and internal supporters. Those facts explain why it persists. They do not establish its future value. Leaders still need to test the business assumption behind the spending against current evidence.

That changes the role of the 2027 planning process. Allocation across technology, people, and programs remains necessary, but it should follow a review of existing commitments. The practical test is simple: would leadership fund the activity today, at its current level, given the evidence now available? That question makes continuation an explicit decision.

Optimization cannot tell you whether the portfolio still fits

Marketing teams use conversion rates, campaign results, channel efficiency, attribution, and productivity to improve execution. These measures show whether an activity is performing against its designed objective. They cannot establish by themselves whether that objective remains important enough to deserve the same resources. That requires a portfolio-level judgment.

Consider a campaign whose conversion rate improves after several rounds of optimization. The team has become better at producing the measured outcome. Leadership still has to decide whether that outcome creates enough business value to justify continued investment compared with competing uses of the same people and money. Execution evidence informs that decision without settling it.

A yearly budget can impose useful resource discipline, while checkpoints allow leaders to revisit assumptions that may change during the cycle. At those checkpoints, the question shifts from how well an activity operates to whether its underlying rationale still holds. This creates a formal route for changing an allocation when the evidence changes.

Adding initiatives does not perform that review. AI pilots, programs, channels, content, and campaigns can each have an internal rationale while collectively creating a larger portfolio. Every addition consumes some combination of money, employee time, management attention, and governance effort. A budget process therefore needs a way to reconsider existing commitments as well as evaluate proposed ones.

Measurement can otherwise favor familiar work. A mature program may have a stable dashboard while an emerging use of resources has less established evidence. The dashboard shows what can be measured under the existing program; leadership must decide what those measurements prove about future allocation. Measurement is evidence for the decision rather than a substitute for it.

Test whether AI investment changes decisions

AI makes this distinction concrete. A CMO can approve pilots, deploy tools, automate tasks, and expand usage while leaving ownership, workflows, and resource allocation unchanged. A useful planning test is whether an experiment has produced enough evidence for a decision: scale it, continue testing it, redesign it, or stop it. The experiment earns further resources through what the organization learns and can act on.

One failure mode is “automating ambiguity”: applying automation where ownership or workflow remains unclear. Treat that phrase as a hypothesis about operating design. Leaders can test it by identifying the decision owner, the workflow being changed, the intended outcome, and the evidence required before committing more resources. If those elements remain unclear, further automation does not resolve the underlying decision problem.

Experiment portfolios also consume scarce resources. Each pilot can require employee time, management attention, integration work, evaluation, and governance. A CMO can therefore review experiments based on the decisions they have enabled and the evidence those decisions produced. The relevant result is evidence strong enough to justify a change in commitment.

Some experiments may warrant substantial additional investment because their results support a valuable use case. Others may need more evidence or a different operating design. This makes AI budgeting a selection problem. The budget follows demonstrated learning and an explicit resource decision.

Make stopping decisions part of the 2027 marketing budget process

A 2027 budget can make continuation decisions explicit alongside proposed additions. Every continuing activity uses resources that could go elsewhere. Leaders should create a formal point at which established work must justify its place in the portfolio. That review makes opportunity cost visible.

Start with areas where leadership sees a credible opportunity to create business value. Then examine which current activities compete for the same people, money, and management attention. Familiarity, available metrics, mature processes, and internal sponsorship can explain why an activity persists. The funding decision should rest on current evidence for its business rationale.

Divestment here means withdrawing resources from an activity or assumption that leadership no longer judges worth its opportunity cost. A competently run program can still lose priority if another use of the same resources has a stronger case. Likewise, a segment or channel can receive less investment when evidence no longer supports the previous level of concentration. The decision concerns resource allocation based on current priorities and evidence.

A practical sequence is focus, divestment, freed capacity, and concentrated reinvestment. It forces leaders to identify where resources for a new priority will come from. Without that step, successive priorities can simply accumulate. The budget grows more complex even when leadership intends to sharpen its choices.

Evidence should determine which commitments survive the review. Leaders can ask whether current results still support the business rationale, whether the success measure represents an outcome leadership values, and whether another use of the same resources has stronger evidence behind it. These questions separate competent execution from the decision to keep funding the work. They make continued investment an affirmative choice.

The review should also cover assumptions that span several budget lines. A segment strategy, channel mix, measurement model, or planning cadence can shape many programs at once. Leaders can identify investments that depend on the same assumption and examine them together. This reduces the risk of stopping one program while preserving the same questionable premise across the portfolio.

Reallocation should follow those judgments. Once resources are freed, leadership can place them where the evidence supports further investment or learning. The decision becomes observable: executives can record what was reduced, where the resources moved, what result was expected, and what happened afterward. That record provides a basis for testing whether the reallocation worked.

Treat adaptability as a hypothesis

A disciplined reallocation process does not establish that divestment, concentration, or faster changes will produce higher growth or greater resilience. Those outcomes are hypotheses that require evidence from subsequent decisions and results. Budget planning should therefore distinguish the quality of the decision process from the business outcome it is intended to produce.

Executives can record which assumptions they tested, which activities lost resources, where those resources moved, and what evidence would justify maintaining or reversing the decision. Later reviews can compare the expected outcome with what occurred. This creates a feedback loop between planning and evidence, while giving leadership an explicit mechanism for reversing a prior allocation when its rationale weakens.

Key takeaways for leaders

• Test existing commitments before adding budget: CMOs preparing 2027 plans can require established activities to justify their current funding against fresh evidence. Continuation becomes an explicit allocation decision rather than a default.
• Separate execution performance from portfolio value: Strong conversion rates, attribution, or channel efficiency show how well an activity performs its assigned job. CMOs still need to judge whether that outcome merits resources compared with competing priorities.
• Make AI experiments earn further investment: CMOs can evaluate AI pilots by the decisions and evidence they produce, including whether to scale, redesign, continue testing, or stop. Pilots with unclear ownership, workflows, or intended outcomes need operating decisions before additional automation.
• Build stopping decisions into budget reviews: CMOs can identify which current activities compete with higher-priority opportunities, withdraw resources where the business case has weakened, and concentrate freed capacity where evidence is stronger.
• Test whether reallocation improves results: CMOs can record which assumptions changed, where resources moved, expected outcomes, and criteria for reversing the decision. Later reviews can determine whether reallocation actually improved business performance and adaptability.