Realistic 90 Day AI Plans

A Realistic 90-Day AI Plan

This is the last issue in the series. Thank you for sticking with me through the last three pieces. You’ve learned more about the unglamorous side of AI work than most in our field are willing to share. That matters.

Quick recap of where we’ve been:

Issue 1: The gap isn’t in your strategy. It’s in the execution. Most teams have the deck and not the workflow.

Issue 2: Most AI projects start at the tool, which is the wrong place. The 5P Framework by Trust Insights™ forces you to start with Purpose, the measurable question, and end with Performance. That’s how you know whether any of it actually worked.

Issue 3: Stop running five pilots. Use TRIPS — Time, Repetition, Importance, Pain, Sufficient Data — to pick the one that pays. Finish it. Then earn the right to do the next one.

Today, data is the part that can ruin everything. People rarely mention it until something goes wrong.

The problem with most AI roadmaps

Most of the AI roadmaps I see are built like the IT roadmaps of 2015. Twelve months. Quarterly milestones. A Gantt chart with a lot of color. Someone spent a long weekend in Smartsheet making it look beautiful. Don’t get me wrong; those roadmaps have a special place in my heart and that’s how I always want to get started as well.

It is, however, almost without exception, fiction in 2026.

The technology you’re planning around will change in the time it takes to print the deck. The team that’s supposed to execute the plan will hit a snag in week three that wasn’t in the model. The vendor you bet on will release a feature in month two that makes half of your plan obsolete. The reorg you didn’t see coming will reshuffle your owner.

Twelve-month AI roadmaps don’t fail because the planners were bad. They fail because the planning unit is wrong for the rate of change.

Ninety days is a better unit. Short enough that the technology won’t outrun you, the org won’t reshuffle out from under you, and the team will remember what they committed to. Long enough that one real workflow can go from “we picked it” to “it’s in production and the metric is moving.”

One workflow. One owner. One metric. One decision at the end. That’s the plan.

And under all that sits the one thing that decides whether any of it actually works.

The data conversation almost nobody has

Here’s the part I really need you to hear. You can have the best strategy and model. You can also pick the right use case. But if your data is flawed, the AI will still produce junk.

Data quality is the silent killer of AI initiatives, and it’s the one thing leaders consistently underinvest in because it’s invisible until it isn’t.

In the AI-Ready Strategist course, we walk through what we call the 6C Data Quality Framework. It’s six dimensions you have to interrogate before you let AI near your business data:

• Clean: Free of errors, duplicates, and formatting inconsistencies. Garbage in, garbage out isn’t a cliché — it’s a law.
• Complete: No missing information. A dataset that’s 80% complete might look fine in a dashboard. The missing 20% is often the 20% that changes the conclusion.
• Comprehensive: The data answers your question directly, not just any question with available data. Most teams go wrong here, analyzing the data they have instead of the data they need.
• Calculable: Usable by a business person, not just a data engineer. If your revenue column has dollar signs and commas mixed with text, it is not calculable. Numbers should be numbers. Dates should be dates.
• Chosen: The opposite of comprehensive. Where Comprehensive asks “do I have enough?” Chosen asks “do I have too much?” A dataset with 200 columns when you need 10 isn’t thorough — it’s overwhelming.
• Credible: Sources are trustworthy and collection methods are sound. You could defend them in a conversation with a skeptical executive. If you can’t, the rest doesn’t matter.

If the answer to any of those is “I don’t know” or “mostly,” you have a data project before you have an AI project. That’s not a setback. That’s the actual order of operations. Skipping it creates the AI horror stories you’ve heard. For example, a chatbot confidently shares false info. A forecasting model might suggest ordering double the needed inventory. A report can be wrong, and no one notices until the board meeting.

Good data governance isn’t bureaucracy. It is the thing that makes the AI safe enough to actually use.

Where this leaves you

Four issues in, here’s the honest summary.

You probably have a strategy. You probably haven’t run it through the 5P lens, so Purpose and Performance are still fuzzy. You probably have too many pilots and not enough owners. TRIPS would have told you to pick one. And you probably haven’t done the 6C data work that keeps the rest from producing garbage.

That isn’t a criticism. It’s the situation almost every team is in right now, including a lot of teams much bigger than yours with more resources. The companies that are going to pull ahead in the next twelve months aren’t the ones with the cleverest strategy. They’re the ones who do the unglamorous middle work first.

That’s what The AI-Ready Strategist is for. It’s the three frameworks, 5P, TRIPS, and 6C, that turn “we have a strategy” into “we have an AI workflow producing measurable results.” You’ll also have access to the tools and templates we use with our own consulting clients. Not a magic wand. Not another deck. A structured walk through the actual work.

If you’ve read this far, you already know whether this is for you.

👉 The AI-Ready Strategist: trustinsights.ai/aistrategycourse

Thank you for reading this series. It’s the most honest look at AI adoption in a real business. If any of it resonated, forward it to one person on your team who needs to hear it. That’s the most useful thing you can do with it.

How is your AI adoption going? Reply to this email or join the conversation in our Free Slack community, Analytics for Marketers!

– Katie Robbert, CEO

Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.