The internal misalignment on AI is the single greatest blocker to adoption. Leadership mandates, "We need AI to be competitive in 2026," while the implementation team, already overworked and under-resourced, feels paralyzed. This framework was recently presented by Katie Robbert of Trust Insights at the Marketing Profs conference and provides the necessary structure to bridge that organizational gap.
Here is a recap of Katie Robbert's Talk. Trust Insights works with companies to help with Marketing Strategy and Analytics
Every decision you make is, literally, change management. This guide provides four foundational, repeatable frameworks that translate the C-suite mandate into a viable, tactical plan. These principles apply to AI today, but they will apply equally to whatever the next flashing technology is tomorrow.
1. The TRIPS Framework: Where to Start with Automation
The first strategic move is prioritizing the right use case. When faced with a long list of possibilities, the TRIPS framework helps you quantify and score projects based on maximum value and minimum risk. Projects should be scored on a simple 1 to 5 scale.
T: Time Consumed
Quantify total time spent: The more time a task consumes (whether it's frequent and short, or rare and long), the greater the immediate value of automation.
R: Repetitiveness
Tasks done the exact same way over and over are easier for AI to learn and execute efficiently. High repetitiveness means lower training cost and higher reliability.
I: Importance / Security
Start with low-to-medium importance tasks. Avoid starting with tasks involving Protected Health Information (PHI), deep financial data, or core data security. Build trust with a low-impact status update or internal reporting before automating customer-facing product launches.
P: Pain
This is arguably the most critical factor. How much does your team hate doing this task? High-pain tasks lead to procrastination, drag, and inconsistency. Automating a hated task yields the fastest morale and velocity improvements.
S: Quality Data
Do you have sufficient, high-quality data to train the AI? If the data is messy, incomplete, or biased, the output will be garbage. Data quality is non-negotiable.
The tasks with the highest total TRIPS score are your starting point, moving the discussion immediately from debate to data.
2. The 5P Framework: Securing Your Foundation
Once you know what to automate (via TRIPS), you must ensure your organization is ready how. The 5P framework—a versatile change management model—ensures you don't fall into the common trap of leading with technology.
- Purpose: Non-negotiable. What specific business problem does this AI solve? (e.g., Reduce time spent generating reports by 25%). If the purpose is vague, stop.
- People: Broadly map everyone involved: end-users, customers, stakeholders, and other teams that will be impacted. Ignoring stakeholders ensures team resistance and later-stage project crushing.
- Process: Document the process, but don't let documentation be a blocker. Use video recording or simple generative AI to document complex workflows. Understand the "As-Is" state before moving to the "To-Be" state.
- Platform: (Third, Not First!) Only now do you select the tools. Do not let tool selection define your strategy. Choose technology that fits the Process and serves the People, avoiding the "shiny object" that doesn't meet the need.
- Performance: The other bookend. These are your success metrics. Go back to your purpose—did you solve the problem?
3. The C4 Data Quality Framework: Trusting Your Inputs
AI success depends entirely on the data it consumes. Many organizations focus on predictive or proprietary data before securing their foundational data, causing collapse. The C4 framework provides simple, non-technical checkpoints for data governance:
- Clean: Are there duplicate records? Are outliers addressed? Is the data format consistent?
- Complete: Are all required fields populated? Do you have sufficient historical data?
- Capable: Can the data be easily accessed and analyzed by your current tools? Does the format work with your platform?
- Credible: Is the data collection methodology sound? Do you have confidence in the source, and is the data validated against external benchmarks? (Avoid building surveys simply to confirm an internal headline.)
4. The Quantifiable ROI Framework: Proving Value
The most common barrier to scaling AI is the failure to prove ROI. You cannot measure ROI on something you are not measuring today. The solution is the AI ROI Calculator (or "labor saved").
The Simple ROI Calculation:
Focus on the variable cost (time spent by a person) and the variable gain (time saved by automation).
Example (Report Generation):
- Baseline (B): A task takes 5 hours per week (20 hours/month). Cost is $20/hour. Monthly Baseline Cost = $400.
- Automated (A): The new AI process reduces the task to 1 hour per week. Monthly Automated Cost = $80.
- ROI Formula: (B - A) / A = (400 - 80) / 80 = 4.0, or 400% ROI.
This simple quantification transforms AI from a nebulous cost to a calculated investment.
The Final Matrix: Prioritizing Execution
To finalize your plan, plot every high-scoring project onto a simple Feasibility vs. Impact matrix:
- Impact Score: The average of your TRIPS Score and your ROI Calculation.
- Feasibility Score: Derived from your organizational readiness (the score of your 5P Framework).
This final matrix visually separates Quick Wins (High Impact, High Feasibility) from Strategic Bets and Time Wasters, giving you the necessary tool to present a data-driven, defensible AI roadmap to your leadership.
