INBOX INSIGHTS: AI Decisioning, Deep Fact Checking - Trust Insights Marketing Analytics Consulting

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INBOX INSIGHTS: AI Decisioning, Deep Fact Checking (2025-09-17)

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Should You Let AI Make Your Decisions?

Last week, I found myself standing in my kitchen at 5 AM, staring at my coffee maker while my brain was already spinning with the day ahead. Forty-seven decisions awaited me before 9 AM: which client email to prioritize, whether to reschedule that strategy call, how to adjust our content calendar based on yesterday’s analytics.

That’s when the thought struck me: What if I could strategically delegate some of these decisions?

The idea was both exciting and daunting. Because while AI decisioning has become remarkably sophisticated, the line between “this could transform my efficiency” and “this could damage my business” comes down to one critical factor: genuine readiness.

Understanding AI Decisioning Beyond the Marketing Hype

Let’s establish clarity around what AI decisioning actually entails. It’s not about surrendering control to some autonomous system. Rather, it’s automated decision-making based on data patterns and the specific parameters you establish.

Consider this example: instead of manually deciding when to send follow-up emails to leads who download your resources, AI decisioning can automatically trigger those emails, optimize send times, and personalize subject lines based on individual user behavior.

The AI analyzes relevant data points—download time, traffic source, previous engagement patterns—and makes decisions within the framework you’ve defined. The sophistication lies in the nuanced application of your business rules at scale.

Critical Boundaries: Where AI Decisioning Should Never Go

Early in my career, I made the mistake of over-automating too quickly. The lesson was expensive and memorable.

AI decisioning is inappropriate for:

High-stakes, irreversible decisions. Personnel changes, major strategic pivots, or significant financial commitments require human judgment, contextual understanding, and intuitive assessment that AI cannot provide.
Situations requiring emotional intelligence. Customer complaints, team dynamics, or sensitive client relationships demand human empathy and nuanced communication.
Processes you cannot clearly articulate. If you cannot thoroughly explain your decision-making framework to your team, you’re not prepared to delegate it to AI.
Brand voice and strategic messaging. Authentic communication reflects your unique perspective and values—elements that generic AI cannot authentically replicate.

The 5P Readiness Assessment: Your Strategic Foundation

Before implementing any AI decisioning system, you must honestly evaluate your organizational readiness. Personally, I’d suggest using our 5P Framework to get started.

Purpose: Have you clearly defined the strategic objectives for implementing AI decisioning? Technology adoption requires clear business justification beyond trend-following.
People: Does your team understand which decisions will be automated and the rationale behind this change? Are they equipped to intervene when necessary? Have you addressed their concerns about the transition?
Processes: Are your current decision-making workflows documented, optimized, and consistent? Automating inefficient processes simply scales inefficiency.
Platforms: Does your technical infrastructure support AI decisioning requirements? Are your systems sufficiently integrated to provide the data quality AI needs for reliable decisions?
Performance: How will you measure success? What specific metrics will indicate whether AI decisions outperform human ones?

Most organizations fail at AI decisioning because they skip this fundamental assessment, rushing toward the technology without establishing the necessary groundwork.

Your Vendor Evaluation Cheat Sheet

When you’re ready to assess AI decisioning solutions, you need to cut through the sales presentations and ask the questions that actually matter. I’ve been in too many vendor demos where the technology looked impressive. And then we got into the specifics and it all fell apart.

Here are the essential questions we use in our vendor evaluation process, organized around the areas that will make or break your implementation:

The Integration Reality Check:

How does this actually integrate with our existing systems? (Get specifics, not vague promises about “seamless connectivity”)
What technical requirements do we need to meet, and do we have them?
What’s the real implementation timeline for someone like us?

The Security Deep Dive:

How is our data handled, stored, and protected?
What security certifications do you maintain, and can you prove it?
What happens if there’s a data breach? (Have a plan, not just insurance)

The Bias Reality:

What data trained your model, and how do you handle bias?
Can we monitor the AI’s decisions for fairness issues?
What’s your process when bias is discovered?

The Support Test:

What training and onboarding do you actually provide?
How do you handle it when things go wrong?
Can you show us case studies from businesses similar to ours?

The Future-Proofing Questions:

How does this scale as we grow?
What’s your product roadmap, and how stable is your company?
How do you measure ROI, and what should we expect?

The key is getting specific examples and references you can actually verify. Any vendor who can’t provide concrete answers to these questions isn’t ready for your business. Don’t forget, you can also ask for references, just like your would in any interview. If they aren’t willing to provide some, you might want to move on to a different vendor.

Your Strategic Implementation Roadmap

Here’s how to begin this evaluation process strategically:

Identify one repetitive decision in your daily workflow. Start with manageable scope—perhaps with highly repetitive tasks of lower priority.
Document your current decision-making process thoroughly. What factors influence your choices? What data do you consider? Be brutally honest about whether this process actually exists anywhere besides your head.
Conduct the delegation test. Could you teach someone else to make this exact decision consistently? If not, you’re not ready for AI implementation.
Download our AI Ready Marketing Strategy Kit to access a foundational version of the 5P assessment and begin evaluating your organizational readiness.

The objective isn’t replacing human judgment with artificial intelligence. It’s strategically freeing your cognitive capacity for decisions that genuinely require your expertise, creativity, and strategic insight.

Because ultimately, your most valuable contribution isn’t making routine decisions—it’s applying your unique strategic thinking where it creates the greatest impact.

What decision are you handing over to AI? Reply to this email or join our free Slack group, Analytics for Marketers.

Ready to develop a comprehensive AI strategy? The AI Ready Strategist Course provides the complete framework for confident AI evaluation, implementation, and optimization—including our full vendor evaluation templates and 5P assessment tools. Strategic preparation today determines your competitive advantage tomorrow. Get your seat here:trustinsights.ai/aistrategycourse

– Katie Robbert, CEO

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss AI decisioning, the latest buzzword confusing marketers.

You will learn the true meaning of AI decisioning and the crucial difference between classical AI and generative AI for making sound business choices. You’ll discover when AI is an invaluable asset for decision support and when relying on it fully can lead to costly mistakes. You’ll gain practical strategies, including the 5P framework and key questions, to confidently evaluate AI decisioning software and vendors. You will also consider whether building your own AI solution could be a more effective path for your organization. Watch now to make smarter, data-driven decisions about adopting AI in your business!

Watch/listen to this episode of In-Ear Insights here »

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In this week’s Data Diaries, let’s talk about a powerful use case for AI deep research that almost no one knows about:

Fact checking.

With any given documnet you have, you can give the AI deep research tool of your choice a document and have it thoroughly research what you’ve written.

Why? Because deep research tools are essentially nothing more than AI agents. They are a series of processes that perform document retrieval, extraction, summarization, and synthesis tasks, and as such are ideally suited for fact checking.

Here’s an example prompt I used recently to validate a paper I’m working on for Trust Insights about how AI Overviews work:

Fact check these documents against Google’s research and patents. Where claims are verifiable, cite your sources that validate the claims. Where claims are not verifiable from the documents and existing sources provided, attempt to provide citations and new sources with correct information. Score claims on a scale of 1-10 where 10 is completely verified by sources, and 1 is completely unverified. All official sources, such as papers and research by Google employees (identifiable by a @google.com email) as well as all other official Google sources – patents (USPTO, WIPO), published research, etc. – should be considered.

I gave the AI deep research tools the rough drafts, and had them go to work to verify what I had written. It worked through the source documents (which all have correctly formatted APA citations to make research easier) and then went, read those source documents, validated the claims, and in 3 instances spotted factual errors that I had to go and fix.

The key takeaway here is that Deep Research tools are really AI agents wearing a librarian’s sweater vest. They’re incredibly powerful and versatile, but you have to know that their limits are much greater than the way they’re marketed. For any task that involves information retrieval, try performing it with a Deep Research tool and see how it goes.

Almost every AI course is the same, conceptually. They show you how to prompt, how to set things up – the cooking equivalents of how to use a blender or how to cook a dish. These are foundation skills, and while they’re good and important, you know what’s missing from all of them? How to run a restaurant successfully. That’s the big miss. We’re so focused on the how that we completely lose sight of the why and the what.

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One thought on “INBOX INSIGHTS: AI Decisioning, Deep Fact Checking”

IPL says:

September 17, 2025 at 4:04 pm

This article is incredibly helpful! The practical questions for evaluating AI solutions are exactly what I needed to move beyond hype and ensure strategic implementation.

One thought on “INBOX INSIGHTS: AI Decisioning, Deep Fact Checking”

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Watch This Newsletter

Should You Let AI Make Your Decisions?

Understanding AI Decisioning Beyond the Marketing Hype

Critical Boundaries: Where AI Decisioning Should Never Go

The 5P Readiness Assessment: Your Strategic Foundation

Your Vendor Evaluation Cheat Sheet

Your Strategic Implementation Roadmap

One thought on “INBOX INSIGHTS: AI Decisioning, Deep Fact Checking”

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