INBOX INSIGHTS: AI Integration Strategy Part 3, AI Data Privacy
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Approaching AI Integration Strategically - Part 3: Implementation
I once sat through a painful meeting where an executive asked for a status update on a new tech implementation. When the project lead proudly announced they had purchased licenses and completed initial training, the executive asked, “But what results are we seeing?”
Yeeesh. There is no pleasing some people.
In parts 1 and 2 of this series, I covered the STEM and 5P frameworks for AI strategy. Today, I want to focus on what happens after the strategy is in place: implementation planning. Because let’s be honest – this is where most AI initiatives either succeed spectacularly or crash and burn.
This is a bit of a longer post because, well, implementation is hard. Scratch that. Implementation is easy. Good, sustainable and measurable implementation is hard.
The Implementation Roadmap: More Than Just “Turn It On”
When I talk to organizations that are struggling with AI adoption, I often find they’ve skipped creating a proper implementation roadmap. They went from strategy to execution without the critical planning step in between.
A solid AI implementation roadmap should include:
1. Phased Approach
When you’re excited about AI, you want to implement everything at once. But trust me on this: a phased approach works better.
At Trust Insights, we recommend breaking AI implementation into distinct phases:
I worked with a client who wanted to implement AI for everything simultaneously. You should have seen their list – it included customer service, marketing, HR, and internal operations. It was overwhelming to look at. Instead, we convinced them to start with email response automation – a contained use case with measurable impact. The lessons they learned in that pilot (about data quality, user adoption, and performance evaluation) proved invaluable when they expanded to other areas.
2. Timeline with Dependencies
Your implementation timeline needs to identify not only when things will happen, but what dependencies exist between different elements.
For example, before you can train users on your new AI system, you need to:
Mapping these dependencies helps avoid the dreaded situation where you’ve promised results by a certain date but haven’t accounted for all the preliminary steps required.
I once had a client insist they could roll out their AI content system in two weeks. When we mapped the dependencies, including data migration, team training, and workflow reconfiguration, it became clear that eight weeks was more realistic. Setting accurate expectations early prevented what would have been a very uncomfortable conversation later.
3. Resource Allocation
This is where I see most implementation plans fall short: they do not specify who will do what.
Your implementation plan should clearly identify:
Remember in Part 2 when we talked about “People” and who needed to be involved? This is when you’ll use that analysis. I’ve seen too many AI projects assigned as “side jobs” to people who are already at full capacity. Surprise! Those projects rarely succeed.
I remember one organization where the marketing director casually told a team member, “Oh, and you’ll be leading our AI implementation. It shouldn’t take much time.” Six months later, nothing meaningful had happened because the person had a full-time job already. The AI initiative became that thing they’d get to “when there’s time” – which, of course, was never.
Preparing Your Team: The Human Side of Implementation
Let’s talk about something that gets overlooked way too often: preparing your people for AI implementation.
Even the most sophisticated AI system will fail if your team doesn’t adopt it. Here’s how to set your team up for success:
1. Address the Fear Factor
Real talk: when you announce an AI implementation, many of your team members will immediately think, “Is this going to replace me?”
Instead of ignoring this fear, address it directly. Be transparent about:
2. Develop a Training Program
Training for AI implementation should go beyond “how to click buttons.” It should include:
3. Create Champions and Support Systems
Every successful AI implementation I’ve seen has had internal champions—people who are excited about the technology, learn it thoroughly, and help others adopt it.
Identify these potential champions early and:
At a different company when we launched a new project management system (not AI, but the principle is the same), I made sure to identify champions in each department. These weren’t necessarily the most senior people – they were the ones who showed genuine interest. They became our first line of support, and their enthusiasm was contagious.
Additionally, establish ongoing support systems like:
Next Steps: Moving from Implementation to Measurement
Even the best implementation plan is meaningless without a way to measure its impact. In Part 4 of this series, I’ll dive deep into creating a measurement framework for your AI initiatives—because if you can’t measure it, you can’t improve it (or justify the investment).
I’ll cover:
In the meantime, I’d love to hear: What part of AI implementation do you find most challenging?
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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Data Diaries: Interesting Data We Found
In this week’s Data Diaries, let’s talk about AI data privacy. I recently had the opportunity to speak at the Trace One User Conference in Miami, and one of the top questions on everyone’s minds around AI was data privacy. When is your data private? When is it not?
To clarify this, let’s first set some basic definitions. An AI model is software written by AI, for AI, in the same way that Microsoft Word is software written by humans, for humans. When we talk about AI models, we’re talking about the engines that do the data processing, converting prompts into outputs.
No one ever uses an AI model directly; they’re basically giant databases of statistics, if you were to open one up. Instead, we have interfaces to those models, tools like ChatGPT that talk to a model. You’ve seen this whenever you tap on the model picker in ChatGPT or Gemini and receive a bewildering, poorly-named list of choices - o3, o4-mini, o4 mini-high, GPT-4.5, GPT-4.1, etc. Those are the models, and ChatGPT is the interface.
This is important to know because, from a data privacy perspective, the interface is where privacy issues occur. The models themselves can’t and don’t ever learn directly from us. They’re massive pieces of software that take months to build and train. Like the engine of a car, we don’t interact with it directly. We interact with a steering wheel, seats, tires, the radio, etc.
What AI providers do, depending on privacy policies, is capture the data that you put into their interface and capture the data that comes out of the interface. That’s where you can lose the privacy of your data.
This is important, especially for understanding the difference between a model and an interface. DeepSeek, for example, is an interface on the company’s models. If you download their models (which you can) and run them on your own hardware, you have to provide the rest of the car - the interface. The same is true of every open weights model - you download their models engine, and then you put it in a car you have to provide.
That means that you control how data is collected and used. Your data is as safe as the rest of your infrastructure, be it a laptop, a server, or a huge, expensive AI cluster.
When you use someone else’s interface, you are subject to their privacy policies. DeepSeek’s web interface and app state clearly in their Terms of Service that they capture every bit of data that goes in and out of their model when it’s run in their interface. You get in their car, and they track everything you do.
The same is true of the free versions of most AI tools. The classic rule applies:
If you’re not paying, you (and your data) are the product that the company sells to someone else.
If your employees are using AI at work that you’re not paying for, they’re almost certainly leaking your confidential data to someone else. The solution for this is straightforward: pay for AI tools for your employees to use, and apply the same data governance and data security policies to those tools that you already have.
What’s not effective? Telling employees they can’t use AI at all. First, some employees will anyway, whether you want them to or not. Second, in doing so, you handicap your company’s capabilities compared to competitors that have strong AI policies and provide approved tools to their employees.
Think back to the early 2000s, when companies needed to figure out smartphones. Those companies that outright banned them had less satisfied employees who used them anyway, against company rules, and your data found its way onto devices outside your control. Those companies that figured out device management and paid for company devices for employees to use found greater adoption, greater satisfaction, and greater data security.
That’s where AI is today. Know what the privacy policies of different AI services are, and provide your employees with the tools and security that meet both your needs.
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