This post was originally featured in the April 23rd, 2025 Newsletter found here: INBOX INSIGHTS, April 23, 2025: AI Integration Strategy Part 3, AI Data Privacy
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:
- Phase 1: Pilot – Select one specific use case with high potential value and low implementation complexity
- Phase 2: Expand – Roll out to additional use cases based on lessons from the pilot
- Phase 3: Integrate – Connect AI systems with broader organizational workflows
- Phase 4: Optimize – Refine based on performance data and user feedback
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:
- Finalize platform selection
- Complete technical setup
- Establish governance guidelines
- Create training materials
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:
- Who is responsible for technical setup
- Who will develop training materials
- Who will provide subject matter expertise
- How much time each person needs to allocate (and what they’re going to stop doing to make that time available)
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:
- How AI will change roles (not eliminate them)
- What new skills team members will develop
- How AI will eliminate tedious tasks and create space for more valuable work
2. Develop a Training Program
Training for AI implementation should go beyond “how to click buttons.” It should include:
- Conceptual understanding: How does the AI work? What are its strengths and limitations?
- Practical skills: How do you use the specific platform? How do you write effective prompts?
- Integration knowledge: How does the AI fit into existing workflows?
- Troubleshooting: What do you do when the AI doesn’t perform as expected?
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:
- Give them advanced training
- Involve them in implementation decisions
- Recognize their contributions
- Allocate time for them to help others
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:
- Regular Q&A sessions
- Troubleshooting channels
- Best practice-sharing forums
- Office hours with experts
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:
- How to establish baseline metrics before implementation
- Defining clear success criteria
- Creating a measurement plan
- Calculating the ROI of your AI investments
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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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.
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