INBOX INSIGHTS, April 16, 2025: AI Integration Strategy Part 2, Survivorship Bias in AI - Trust Insights Marketing Analytics Consulting

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INBOX INSIGHTS: AI Integration Strategy Part 2, Survivorship Bias in AI (2025-04-16)

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Approaching AI Integration Strategically – Part 2: The 5P Framework

Last week, I found myself in a meeting where an executive announced they had purchased an enterprise AI platform subscription. When someone asked how we would use it, the executive’s response was essentially, “Figure it out.”

I watched the room fill with that awkward mix of confusion and forced enthusiasm that happens when leadership skips the strategy part and jumps straight to implementation.

In Part 1 of this series, I talked about using the STEM framework to organize your AI strategy. Today, I want to introduce you to another framework we use at Trust Insights: the 5P Framework. This is especially helpful when you’re trying to implement something complex like AI across your organization.

The 5P Framework for AI Integration

The 5P Framework breaks down planning into five essential components:

Purpose: What problem are you solving?
People: Who needs to be involved?
Process: How will you solve the problem?
Platform: What tools will you use?
Performance: How will you measure success?

Let me walk through each one specifically for AI integration.

Purpose: Defining the Problem AI Will Solve

I can’t emphasize this enough: Start with the problem, not the technology.

When I work with clients on AI integration, I ask them to complete this sentence: “As a [persona], I [want to], so [that].”

If they can’t articulate a clear business problem and outcome, we stop and do that work first. Because here’s the reality: AI is expensive, it requires significant resources to implement correctly, and it will disrupt your existing workflows. You need a compelling reason to go through all that.

Some legitimate purposes for AI implementation include:

Reducing time spent on repetitive, low-value tasks
Scaling personalization beyond what is humanly possible
Analyzing massive datasets to find patterns
Improving accuracy of predictions or recommendations
Accelerating content creation while maintaining quality

Notice that none of these purposes is “because our competitors are doing it” or “because our CEO read about it.” Those aren’t purposes – they’re shiny objects.

People: The Human Side of AI

Here’s where I see most AI implementations start to break down. Companies focus so much on the technology that they completely overlook the human element.

For AI to succeed, you need to consider:

Skills Gap Assessment: What AI-related skills does your team currently have, and what skills will they need? This might include prompt engineering, AI tool configuration, or data preparation.
Training Plan: How will you upskill your existing team? Will you bring in external experts?
Change Management: How will you help your team adapt to new AI-driven workflows? (Spoiler alert: sending one email announcement isn’t enough.)
Roles and Responsibilities: Who will “own” the AI initiative? Who will be responsible for governance and oversight?

A few years ago, I worked with an agency that implemented a powerful CRM but didn’t train their team on how to use it effectively. Six months later, they had spent $60,000 on the platform, and only two people in the entire organization were using it (me, as the admin, being one of them). I remember sitting through a very contentious meeting with the VPs. They were very upset about having “one more thing” to do.

A successful AI implementation (or any tech implementation) requires champions, trained users, and clear ownership. Without addressing the people component, your expensive AI tools will collect digital dust.

Process: Creating AI Workflows That Work

When it comes to integrating AI into your organization, process is everything. You need to think about:

Integration Points: Where exactly will AI fit into your existing workflows?
Human-in-the-Loop Design: How will humans and AI work together? Where is human oversight necessary?
Data Flows: How will data move into and out of your AI systems?
Governance: What rules and guidelines will govern your use of AI?
Quality Control: How will you ensure that the AI is producing acceptable outputs?

I learned the importance of process design the hard way. At a previous company, we implemented a sharepoint/project management system without clearly defining where it fit in our PLC/SDLC. The result was confusion, duplicated efforts, and ultimately, after three failed rollouts, the system was scrapped.

A better approach is to map your existing processes first, then identify specific points where AI can add value. For example, in a content creation workflow, AI might help with:

Initial research and topic clustering
First-draft generation
SEO optimization
Grammar and style checking

But humans might still handle:

Strategic content planning
Expert review and fact-checking
Final editing and approval
Publication and distribution

The key is being intentional about where AI fits and where humans remain essential.

The more detailed your process documentation, the better your odds of a smooth AI integration. If you think you’re getting too “in the weeds,” you’re not. Clear process is the foundation for successful AI.

Platform: Choosing the Right AI Tools

Only after you’ve defined your purpose, people strategy, and processes should you start thinking about which AI platforms to use.

When evaluating AI platforms, consider:

Capability Match: Does the tool actually do what you need it to do? (This sounds obvious, but you’d be surprised how many companies buy tools based on buzzwords rather than actual capabilities.)
Integration Requirements: Will it work with your existing tech stack?
Scalability: Will it grow with your needs?
Total Cost of Ownership: Beyond the subscription fee, what will implementation, training, and maintenance cost?
Governance Features: Does it provide the transparency and control you need?

I’ve seen too many companies start with the platform (“We need [insert trendy AI tool of the week]!”) rather than starting with the problem. That’s like buying an expensive kitchen gadget before knowing what you want to cook.

Instead, your platform selection should be the natural outcome of your purpose, people, and process planning.

Performance: Measuring AI Success

Finally, we come to performance – how you will measure the success of your AI implementation.

This takes us back to the measurement discussion from Part 1, but with more specificity:

Baseline Metrics: Document your current performance on key metrics before implementing AI.
Success Criteria: Define what “good” looks like. Is it a 20% reduction in time? A 30% increase in output? A 15% improvement in accuracy?
Measurement Plan: Determine how and when you will collect data to evaluate your AI implementation.
ROI Calculation: Establish how you will calculate the return on your AI investment.
Feedback Loop: Create mechanisms to gather qualitative feedback from users and stakeholders.

When I work with clients on AI implementation, I insist that we establish these measurement criteria before we even start looking at platforms. Why? Because without clear success metrics, you’ll never know if your AI investment is worthwhile.

Putting It All Together: The 5P AI Strategy Document

Now, let’s put all this together into a simple AI strategy document template:

Purpose Statement: “As [insert company] we [want to integrate AI], so [that we can solve a specific business problem and reach a defined goal].”
People Plan:
- Skills required
- Training plan
- Roles and responsibilities
- Change management approach
Process Design:
- Current workflow mapping
- AI integration points
- Human-in-loop design
- Quality control mechanisms
Platform Selection Criteria:
- Required capabilities
- Integration requirements
- Budget constraints
- Evaluation method
Performance Metrics:
- Current baseline metrics
- Success criteria
- Measurement approach
- Review timeline

This doesn’t need to be a 50-page document. In fact, I prefer a concise 2-3 page strategy that clearly articulates each of these five elements.

Get your copy of the 5P Framework here

Start Small, Learn Fast

One final piece of advice: start small with your AI implementation. Pick one well-defined use case where AI can deliver clear value, and use that as your learning opportunity.

I worked with a client who wanted to implement AI across their entire content marketing operation simultaneously. Instead, I convinced them to start with just one content type for one product line. This allowed them to learn, refine their approach, and demonstrate value before scaling – ultimately leading to a much more successful organization-wide rollout.

In Part 3 of this series, I’ll dive deeper into implementation planning and change management for AI integration.

In the meantime, I’d love to hear what aspect of the 5P Framework you find most challenging when it comes to AI strategy.

Reply to this email to tell me, or come join the conversation in our free Slack Group, Analytics for Marketers.

– Katie Robbert, CEO

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This week, let’s talk survivorship bias. This is a phenomenon in statistics that has broad applications outside of stats.

In a nutshell, survivorship bias is when your data is skewed by what survives and what doesn’t. The classic example is the examination of bombers from World War II. When bombers returned from bombing runs, engineers looked at the damage they took – to places like the wings and the tail. They concluded that planes needed reinforcement there.

Well… no. The survivorship bias is that a whole bunch of planes didn’t return. Those planes were hit in other, more critical areas, areas where the plane didn’t return. If you’re going to add armor anywhere, add armor to the places the damaged planes didn’t get hit in – like the cockpit. Those are the places that presumably non-returning planes got hit and didn’t survive.

You see this in marketing all the time. Every quarterly board report, every marketing awards ceremony… all showcase what worked. That’s great – but you never get the whole picture, and you especially NEVER get what didn’t work. Which means if you try to replicate the award-winning formula, there’s probably a ton of implicit knowledge from all the failures that you don’t get, and you’re doomed to repeat those failures in your own adoption.

This is something we discussed on the Trust Insights podcast this week, when people show off their social media success stories with AI. “Look at this cool thing I built!” is fun to see, but it tells you none of the difficult road they took to reach the success. It tells you none of what went wrong, so you know what to avoid. It shows none of the planning, none of the infrastructure, none of the experience that went into making the thing.

That in turn should make you question – does the person who built this thing actually know what they’re doing? Or did they copy and paste it from Reddit without understanding what they were doing and why? Survivorship bias makes that impossible to tell.

One of the hidden challenges of AI is its cost. When you start building automations and agents, you start consuming AI APIs quite a bit – and every time you do, you swipe the credit card. Sharing and knowing the failures, the things that went wrong, the surprises and gotchas that went into the eventual success are so, so important for helping build robust, efficient solutions that won’t slap you with a five figure API bill.

As you read and watch people’s use of AI and listen to their success stories, remember that those successes are built on a foundation of failures, past and present. If they’re not sharing the fails, you’re not getting the whole picture and the information you need to succeed. If you want to combat survivorship bias, ask people what didn’t go well, and learn from what didn’t survive to the final shiny object.

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