Tier 1: What does an AI audit reveal about your marketing operations?
The AI Audit is your diagnostic phase. You can’t optimize what you don’t measure, and you can’t implement effectively without knowing your starting point. This tier involves three parallel workstreams: assessing your team’s current AI maturity, auditing your existing tool stack for AI capability, and mapping specific opportunities where AI will create the most immediate leverage.
The maturity assessment is straightforward. We score your current operations across four dimensions: content production, analytics and reporting, campaign management, and customer engagement. Each dimension gets rated on a scale from 1 (no AI adoption) to 5 (AI-native operations). A score of 2 means you’re experimenting casually. A score of 3 means you have some structured workflows but inconsistent quality. A score of 4 means AI is integrated into most workflows but not optimized. A score of 5 means your team works at AI-native speeds with minimal manual intervention.
The tool stack assessment examines what you’re currently using and what AI capabilities already exist within those platforms. HubSpot has AI copywriting. LinkedIn has audience intelligence powered by machine learning. Google Analytics has AI-driven insights. Most teams don’t realize they already have dormant AI capabilities. This audit surfaces what’s already available and what new tools actually need to be added. The outcome is clarity: you know exactly which tools to activate versus which to purchase.
The opportunity map identifies the workflows where AI provides the greatest leverage for your team. For a content-heavy team, that’s content production and copywriting. For a data-driven team, it’s campaign analysis and audience segmentation. For a performance marketing team, it’s bid management and conversion optimization. This tier doesn’t just say “implement AI everywhere.” It says, “implement AI here first, because this is where it creates the most ROI for your team’s current operations.”
The deliverables from this tier are a written AI Maturity Score across the four dimensions, a detailed Tool Stack Assessment with activation priority, and a prioritized Opportunity Map showing which workflows to tackle in Tier 3.
Tier 2: How do you build team capability and AI guardrails?
You can’t hand your team a new tool and expect them to use it effectively. They need training, guardrails, and a system for building and sharing knowledge. This tier focuses entirely on capability building.
The team training program covers three areas: AI fundamentals (how large language models actually work, where they fail, and their limitations), tool-specific training on the platforms your team will use, and prompt engineering fundamentals. Prompt engineering isn’t magic. It’s systematic instruction design. Your team needs to understand that different prompts produce different outputs, that specificity matters, and that the quality of the instruction directly correlates with the quality of the output.
The prompt library is a shared knowledge repository that your team builds throughout this tier. When someone writes an effective prompt for content ideation, they don’t hoard it. They document it, test it with different inputs, and add it to the library. When someone discovers that a particular AI tool performs better on email subject lines than another, that discovery is recorded. By the end of this tier, your team has a battle-tested prompt library tailored to your brand, products, and audience. New team members onboard against this library. Experienced team members improve it.
The quality control workflow is critical. This is where you prevent AI from degrading your brand voice. The workflow specifies which outputs require human review before publication, which require spot checks, and which are trusted at high volume. For a B2B company, every AI-generated long-form piece might require a senior writer review. Short social posts might get spot-checked weekly. Email subject lines might go to production with only tag-based filtering. These thresholds are specific to your brand risk profile.
The deliverables from this tier are a completed training program for the team, a living prompt library organized by use case, and a documented QC workflow with clear decision rules.
Tier 3: How does AI integration work across your daily workflows?
Implementation is where AI actually moves into your daily operations. This tier focuses on the workflows you identified in the Audit tier, starting with the highest-leverage opportunities.
The implementation process follows a specific pattern for each workflow. You first document the current state: how the workflow runs today, who’s involved, how long it takes, and what the quality measures are. You then design the AI-augmented workflow, mapping where AI gets inserted and how human review fits in. You then build that workflow using your tool stack, starting with simple automation and adding complexity as the team gains confidence.
The typical implementation sequence for a 5-20 person marketing team is: content production first (ideation, outlining, first-draft copywriting), then copywriting optimization (subject lines, email sequences, ad variations), then audience segmentation and targeting, then campaign analysis and reporting. This sequence works because it builds from less risky (your team can easily review AI content before publishing) to more autonomous (reporting often doesn’t require human review).
AI Workflow Maps document exactly how each workflow now runs. These maps show the decision points where AI is making recommendations versus making decisions, where human review is required, and what the quality gates are. A workflow map for content production might show: AI generates 5 content ideas, writer selects 2, AI outlines those 2, writer refines outline and adds brand context, AI writes first draft, writer edits and approves. Clear handoff points. Clear quality gates.
Automation sequences are the technical configurations that make these workflows run at scale. In HubSpot, this might be email sequences with AI-optimized subject lines and send times. On your content management system, this might be scheduled publishing of AI-assisted content. On your analytics dashboard, this might be automated segmentation and reporting. These aren’t complex integrations. They’re practical configurations that move workflows from manual to semi-automated.
Performance baselines establish what the metrics looked like before AI implementation. Time to produce one piece of content. Cost per email sent. Conversion rate on email campaigns. Accuracy of audience segmentation. These baselines are critical because Tier 4 measures everything against them.
The deliverables from this tier are AI Workflow Maps for each implemented process, documented automation sequences, and baseline performance metrics.
Tier 4: How do you continuously optimize and expand AI use cases?
This tier is the continuous improvement loop. You don’t hit the end of week 12 and declare victory. You measure what AI is actually delivering, you optimize based on those measurements, and you expand to new use cases.
The monthly AI impact report shows, for each implemented workflow, how much leverage you’ve gained. The AI Leverage Ratio is the primary metric: it’s the ratio of output per team member after AI implementation to output per team member before. Below 1.0 means AI actually slowed you down (rare, but it happens). 1.0 to 1.5 is basic automation gains. 1.5 to 2.5 is effective implementation and solid ROI. 2.5 and above is AI-native operations where your team works at a fundamentally different speed.
Beyond the leverage ratio, you track quality metrics. For content, this is engagement and conversion rates. For audience segmentation, it’s campaign precision and relevance scores. For reporting, it’s decision velocity and insight accuracy. AI often trades volume for some quality loss early on. This tier’s job is to optimize the tradeoff until you’re gaining both volume and quality simultaneously.
The ROI dashboard shows financial impact. If your team was spending 40 hours a week on content production and you’ve reduced that to 25 hours with AI assistance, what’s the hourly cost of that time? That’s your savings. Against that, what did AI tools cost? That’s your payback period. Most teams reach 2-3 month payback for content workflows, 4-6 months for campaign optimization workflows.
The expansion plan identifies new use cases for AI based on what worked in your first implementation. Maybe content production was so successful that you want to expand to customer success content. Maybe campaign optimization worked, so you want to add pricing optimization. This tier systematizes how you evaluate, validate, and scale new AI workflows.
The deliverables from this tier are monthly AI Impact Reports showing Leverage Ratios and quality metrics, an updated ROI Dashboard, and a quarterly expansion plan identifying next workflows for AI implementation
Growth Strategy and Planning
