How to Use AI in Your Cannabis Business—Today

Key Insights

• AI tools can generate complete cannabis marketing campaign structures - including executive summaries, audience strategies, goal frameworks, and channel recommendations - in seconds, giving cannabis marketing teams a working starting point that would take hours to produce from scratch, but only when specific inputs like budget, target audience, and campaign goals are provided upfront.
• The most important mindset shift for cannabis marketers adopting AI is treating first outputs as drafts to iterate on rather than finished deliverables: approximately half of AI responses will require correction or refinement, and the practitioners who get the most value from AI are those who move quickly through iterations rather than expecting perfection from a single prompt.
• Working in an IDE environment with AI tools like Claude Code gives cannabis marketers a significantly more practical workflow than chat interfaces alone, because it allows them to work directly within their actual campaign files and documents rather than copying and pasting content between separate tools - reducing friction and keeping the working context coherent.
• AI-generated cannabis campaign plans will make assumptions when critical inputs are missing - filling in budget estimates, goal numbers, and audience definitions based on context - which means the quality of AI outputs scales directly with the specificity of the brief provided, and vague prompts produce generic plans while detailed prompts produce directly usable work.
• Cannabis marketers who develop a systematic approach to AI prompting - providing context, specifying constraints, and iterating deliberately - will create a compounding productivity advantage over those who either avoid AI entirely or use it sporadically without a consistent workflow.

Webinar Highlights

00:00 – A Different Format: Live AI Demonstration

Jake Litkey opens by setting expectations for a format departure: rather than a standard conversation, today's episode will show AI tools in action live. Justin Gara - an economics-trained marketer who worked at MediaJel, ran his own agency, and now operates as an AI consultant building an AI product - will demonstrate how cannabis marketers can use AI tools in a real working environment to accelerate campaign planning and content development.

06:00 – Working in an IDE: A More Practical AI Workflow

Justin introduces his preferred working environment: an IDE (integrated development environment) with Claude Code running in the terminal. He explains why this setup is more practical for real marketing work than a web chat interface - it allows him to work directly within his actual folders and files, click through documents while AI processes them, and maintain a coherent working context across a project. The demonstration makes the case that the IDE workflow scales better than chat-only AI use for marketers managing multi-file campaigns.

12:00 – Building a Cannabis Holiday Campaign Plan with AI

The live demonstration builds a cannabis holiday campaign plan using AI. Justin shows how to prompt Claude for an executive summary - specifying the retail focus, Q4 goals, gift-giving angles, stress relief messaging, and premium bundle strategy. When the AI lacks specific inputs on budget or audience goals, it makes assumptions: 25% Q4 increase, 15,000 new customers. The output is a complete structured campaign brief - generated in seconds - that serves as a working document rather than a final plan.

18:00 – Why First Outputs Are Starting Points, Not Deliverables

Justin addresses the most common frustration cannabis marketers have with AI: getting a mediocre or off-target first response and concluding that AI is not useful. His framing is direct - approximately half of AI outputs will require iteration or correction, and that is normal. The value of AI for cannabis marketing is not in producing perfect outputs on the first try. It is in dramatically compressing the time from blank page to working draft, and then in the speed at which marketers can refine those drafts through iteration.

24:00 – Giving AI Better Inputs for Better Outputs

The demonstration covers what happens when you give AI more specific inputs versus minimal context. A vague prompt produces a generic plan with assumed values. A detailed prompt - specifying budget range, target audience, primary KPIs, and channel mix - produces a campaign brief that cannabis marketing teams can actually build from. Justin's guidance is to invest time in the specificity of the brief rather than trying to fix vague outputs after the fact.

30:00 – Building a Repeatable AI Workflow for Cannabis Marketing Teams

The closing discussion frames AI as a tool that rewards consistent, systematic use over sporadic experimentation. Cannabis marketing teams that develop a repeatable workflow - standard prompt structures for campaign briefs, content calendars, audience definitions, and performance reports - will compound their productivity advantage over time. Justin positions the skills being demonstrated not as one-off tricks but as the foundation of a new kind of marketing operations capability.

Frequently Asked Questions

[ {How can cannabis marketers use AI for campaign planning?}

Cannabis marketers can use AI to generate structured campaign briefs, executive summaries, audience definitions, goal frameworks, and channel strategies significantly faster than manual drafting. The process involves providing the AI with specific inputs - campaign objective, target audience, budget range, timeline, and key messages - and then refining the output through iteration. AI is most useful as a high-speed drafting tool that produces a working starting point, not as a system that delivers finished plans without human review and adjustment.

{What AI tools are useful for cannabis marketing?}

Large language models like Claude are directly applicable to cannabis marketing tasks including campaign planning, content creation, brief development, audience research, and performance reporting. Working within an IDE environment using Claude Code gives cannabis marketers a more practical workflow than browser-based chat, because it allows them to work directly within their actual project files and documents. Other AI tools useful for cannabis marketing include image generation tools for creative assets and automation tools for campaign management workflows.

{Why do cannabis marketers get poor results from AI?}

Poor AI results for cannabis marketing typically come from one of two issues: vague prompts that do not provide the specific context AI needs to produce targeted output, or unrealistic expectations that a first output should be final and usable without revision. AI language models make assumptions when inputs are missing, and those assumptions may not match the campaign's actual goals. Providing detailed inputs - budget, audience, KPIs, channel mix - and treating first outputs as drafts to iterate on consistently produces better results than expecting AI to infer everything from a minimal prompt.

{What is the best way to prompt AI for a cannabis marketing campaign?}

The most effective prompting approach for cannabis campaign planning is to be specific about the campaign objective, target audience, budget range, timeline, primary KPIs, and channel strategy before asking AI to generate any output. Including examples of what good looks like - a previous campaign brief or a campaign structure you want to replicate - further improves output quality. When the AI produces a mediocre first response, iterate with corrections and additional context rather than starting over, as the iterative refinement process typically produces better results than repeated cold prompts.

{How do cannabis marketing teams build a repeatable AI workflow?}

Cannabis marketing teams build a repeatable AI workflow by standardizing the prompt structures they use for recurring tasks: campaign briefs, content calendars, audience strategy documents, and performance summaries. Storing these prompt templates and iterating on them based on what produces the best outputs creates a compounding capability over time. Teams that use AI sporadically or without consistent prompt structures get inconsistent results; teams that systematize their AI use across common marketing tasks develop a measurable productivity advantage. ]