MasterControl News Archive

November 19, 2025 by Kelly Starman

Here's a scenario many marketing teams are repeating right now: They compile thorough lists of AI opportunities, present them to leadership, get budget approval—then immediately pick the flashiest option on the list. Six months later, they're sitting in budget reviews explaining why their AI investments haven't delivered measurable results.

The disconnect isn't about choosing bad technology. It's about mistakenly prioritizing novelty over genuine business impact. My team recently identified about 30 problems AI could solve, but we picked exactly two to focus on. When faced with dozens of potential AI applications, it's easy for teams to default to whatever feels most cutting-edge rather than what solves their most urgent problems.

When creating an AI-first marketing program, systematic prioritization becomes your competitive advantage. The difference between AI success and expensive experimentation comes down to one discipline: evaluating opportunities through business fundamentals, not technological sophistication.

Why Scope Creep Kills AI Success

Scope creep is absolutely a “novelty and shiny objects” problem. When teams get excited about AI possibilities, they want to solve everything at once. To build a solid program, however, discipline matters.

Every organization has its own scope, but one or two initiatives is a great place to start, especially considering how much change teams can absorb simultaneously. Ultimately, most AI tools are so new that you'll learn tremendous amounts from narrow, focused implementations. By starting with something concrete that has a clear beginning and end, you can keep from getting distracted by comprehensive solutions that try to solve everything at once.

Look For The Substance, Not The Flash

When evaluating AI opportunities, it's often tempting to eschew broad organizational impact for situational novelty. A specific video generation tool might be impressive, but it won't help you solve your real problems. Choosing situational novelty may give you cool technology, but it only solves a narrow need.

One key is to look for platform solutions that can start with one use case but expand to solve multiple problems. Platform approaches require more upfront thinking but deliver compounding returns.

A key question I like to ask before any purchase: Are we solving one issue with this AI tool, or are we building capability that will unlock multiple future solutions? There will likely be a few concentrated winners in the AI space, so evaluate tools that can grow with your organization.

How To Actually Prioritize High-Impact Problems

When we picked exactly two things to focus on, we understood that our rate of success would go down if we tried to take on more. So, how did we know what to pick?

Our critical evaluation factor was that each initiative needed to enable next steps, not just solve isolated problems. For example, data enrichment work doesn't just improve current processes—it creates foundations for more sophisticated strategies later.

Before evaluating any tool, establish your impact criteria:

Speed to market improvements with measurable timelines. How much faster could this process be? Can you quantify current deployment time and set realistic acceleration targets?

Quality enhancements that scale across touchpoints. Where can AI improve consistency or reduce human error in repeatable processes? Focus on problems that affect multiple teams or customer interactions.

Resource optimization with clear ROI calculations. What does this process actually cost today in time, head count and opportunity cost? Can you redirect those resources to higher-value work?

Ask The Questions That Avoid Expensive Mistakes

The gut-check before deciding on any AI solution is to ask yourself if you can quantify the current cost of this problem. Because if you can't measure the impact of the problem, you can't measure the value of any solution.

Will solving this problem affect your competitive position, or just make existing processes slightly better? Incremental improvements rarely justify expensive AI adoption costs and change management effort.

Are we considering this because it genuinely impacts business outcomes, or because the solution feels innovative? The most successful AI implementations often address mundane but costly inefficiencies.

Don't let marketing costs increase with AI adoption—the point is to reduce costs while improving outcomes. AI took off partly because it was fun to experiment with ChatGPT, but a mindset of play versus performance kills business implementations. Help your team shift from "what cool thing can we try?" to "what expensive problem can we solve?"

Validate Your Assumptions Before You Evaluate

Finally, don't make decisions based solely on leadership perspectives. The gap between how leadership perceives problems and how teams experience them often determines whether AI implementations succeed or become expensive disappointments. To address this, I recently sat down and observed how our sales development reps work to identify the real problems.

Doing so will quickly make it clear that even smart organizations are often tempted toward technically impressive AI solutions that teams don't actually use. A sophisticated customer summary system that populates CRM platforms might be genuinely useful in theory, but might go ignored in practice because they don't fit existing workflows.

Solving problems people care about matters more than building technically impressive capabilities. The most successful AI-first marketing organizations aren't chasing the newest tools; they're tackling carefully selected, high-impact problems. That means disciplined prioritization based on business fundamentals, not technological sophistication.

Focus on measurable business impact, validate assumptions with front-line teams, and resist the temptation to solve everything at once. Get your priorities right first, and the tool selection becomes straightforward. What's the one expensive problem you'll solve with AI before 2026?

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