Speaking & Workshops
An AI marketing workshop built for teams that need results, not hype.
The majority of AI marketing content is written by people who have read about AI, not by people who have deployed it in production campaigns for real companies. The difference is visible immediately: generic prompting tips versus a workflow that produces better ad copy at one-fifth of the cost, or a lead scoring model that improves pipeline quality without requiring a data science team. This workshop is built from what has actually worked in Indian growth-stage marketing contexts.
Why most AI marketing initiatives fail to produce commercial results.
The failure modes for AI in marketing are specific. They are almost never about the technology.
The team is using AI as a faster way to do the same things, not a different way to produce better outcomes.
Using ChatGPT to write a blog post faster is a productivity improvement. Using AI to generate 50 ad copy variants, test them simultaneously, and redirect budget to the best performer within 48 hours is a commercial advantage. The teams that get commercial value from AI are not the ones using it to speed up existing workflows. They are the ones using it to do things that were not previously possible at their budget and team size.
The prompting is generic and the output is indistinguishable from every competitor.
If your AI-generated content sounds like every other company's AI-generated content, you have not gained an advantage. You have contributed to the homogenisation of marketing content. The teams that produce distinctive output from AI tools are investing in prompt engineering, brand voice documentation, and output curation as deliberate skills. Generic prompts produce generic content. The quality of AI output is directly proportional to the quality of the input.
The AI tools are being used in isolation without integration into the marketing workflow.
The content team uses ChatGPT. The paid media team uses a different AI tool. The data team is building something in Python. None of these tools talk to each other, and none of the outputs are systematically used to inform the others. AI creates the most commercial value when it is integrated into a workflow where the output of one AI application becomes the input for another, and where the results are systematically measured and fed back into prompt improvement.
The team has not built an AI governance framework and is creating compliance and quality risks.
AI-generated content that contains factual errors, legal claims that have not been reviewed, or brand voice inconsistencies creates reputational and legal risk. Many marketing teams adopting AI are doing so faster than their governance infrastructure can manage. An AI governance framework defines what can be published without human review, what requires senior review, and what AI tools are not appropriate for given the company's regulatory environment.
Leadership is asking about ROI from AI before the team has had enough time to build a reliable workflow.
The most common reason AI marketing initiatives are abandoned is premature ROI measurement. A team that has been experimenting with AI tools for six weeks is not going to have a measurable revenue impact from those tools. AI workflow development follows a learning curve: the first month is experiment and failure, the second month is workflow refinement, and the third month is when the first measurable outputs start to appear. Cutting the initiative before the learning curve completes destroys the investment made in the first two months.
How AI marketing workshops are structured here.
Use-case selection first. Workflow design second. Live tool application third. Governance and measurement fourth.
Identify where AI will produce the highest commercial value for your specific team
- Current workflow audit: map the marketing team's current workflows and identify the highest-cost, highest-frequency tasks
- AI readiness assessment: data quality, tool access, and team skill levels for the use cases being considered
- Use-case prioritisation: rank potential AI applications by expected value, implementation complexity, and team readiness
- Tool landscape review: which AI tools are most appropriate for the selected use cases given budget and integration requirements
- Baseline measurement: establish the current performance benchmarks that AI will be measured against
- Quick-win identification: the one or two use cases where the team can see results within two weeks of the workshop
Build the specific AI-integrated workflow for each selected use case
- Prompt architecture: the prompt structure for each use case, including system prompts, context, and output format specifications
- Human review checkpoints: where human review is required before AI output is published or acted on
- Quality criteria: the specific criteria by which AI output is evaluated before use
- Feedback loop design: how output quality data is captured and used to improve prompts over time
- Integration points: how AI tools connect to existing marketing tools such as the CRM, email platform, and ad accounts
- Workflow documentation: a written process that any team member can follow to reproduce the workflow reliably
Apply the workflow to real company content during the session
- Live prompt building: participants build and test prompts for their specific use cases during the session
- Output critique: AI-generated outputs reviewed against quality criteria with real-time feedback on what to improve
- Workflow run-through: each selected use case completed end-to-end during the session with the actual tools
- Variation testing: producing multiple output variants from a single input and evaluating which performs best
- Edge case handling: what to do when AI output is unusable, and how to diagnose and fix the prompt
Build the governance and measurement infrastructure to make AI use sustainable
- AI governance policy: what can be published without review, what requires senior review, and what is prohibited
- Brand voice documentation: the specific style guide inputs that ensure AI output matches the brand
- Performance metrics: the specific KPIs that will measure the commercial impact of AI adoption over 90 days
- 90-day AI roadmap: the sequence of use cases to implement over the next three months with milestones
What an AI marketing workshop includes.
Audit and Setup
- Workflow audit
- AI readiness assessment
- Use-case prioritisation matrix
- Tool landscape review
- Baseline measurement setup
- Quick-win identification
Workflow Design
- Prompt architecture
- Human review checkpoints
- Quality criteria definition
- Feedback loop design
- Integration point mapping
- Workflow documentation
Live Application
- Live prompt building session
- Output critique and feedback
- End-to-end workflow runs
- Variation testing practice
- Edge case handling
- Prompt library starter
Governance
- AI governance policy
- Brand voice documentation
- Performance metrics framework
- 90-day AI roadmap
- Tool access and budget guide
- Team skill-building plan
This is right for you if:
- Marketing teams at Indian growth-stage companies that are experimenting with AI tools but have not yet integrated them into a systematic workflow that produces measurable results
- CMOs and marketing leaders who need to build an AI capability within their team and want a structured framework rather than learning by trial and error
- Founders who want to understand which AI marketing applications are commercially viable and which are distractions before investing team time in them
- Performance marketers who want to use AI for creative testing, audience segmentation, and campaign optimisation rather than just content generation
- Content and demand generation teams that are producing large volumes of marketing content and want to scale output without scaling headcount
Not the right fit if:
- Teams looking for a general introduction to AI tools with no connection to their specific marketing workflow, the workshop is applied and practical; a general AI awareness session is a different format
- Organisations that have not yet defined their brand voice and content standards, AI amplifies existing content quality standards and cannot substitute for the strategic decisions that must precede it
- Companies expecting AI to replace their marketing team, the use cases covered are designed to make the existing team more effective, not to automate away the strategy and creative judgment the team provides
Frequently asked questions.
Which AI tools does the workshop cover?
The workshop is tool-agnostic in its framework design but covers the tools most relevant to the selected use cases. For content and copy, that typically includes Claude, ChatGPT, and Perplexity. For visual content, Midjourney, Ideogram, and Canva AI. For marketing automation and lead scoring, the AI features built into HubSpot, Zoho, or whichever CRM the team uses. For paid media optimisation, the native AI tools in Meta Ads and Google Ads. The specific tool selection is confirmed during the use-case audit phase based on the team's current stack and the use cases being prioritised.
How technically capable does the team need to be?
No technical background is required for the majority of the use cases covered. Prompt engineering, workflow design, and output evaluation are skills that any marketer can develop with structured practice. The more technically complex use cases, such as building a lead scoring model or connecting AI tools via API, are covered at a strategic and design level in the workshop and are flagged as requiring technical implementation support beyond the session. The workshop produces a brief for technical implementation even for the more complex use cases.
What are the most valuable AI use cases for Indian B2B marketing teams?
Based on deployment across Indian growth-stage companies, the highest-value AI use cases for B2B marketing teams are: ad creative variation and testing, where AI can generate and test ten times more variants per week than a human team; personalised email sequences, where AI-assisted personalisation of outbound email improves reply rates significantly; content repurposing, where a single piece of original research is transformed into blog posts, social content, email newsletters, and ad copy; and lead scoring, where AI applied to CRM behavioural data improves the quality of leads passed to sales. These four use cases produce commercial results within 60 to 90 days for most teams.
How do you measure the ROI of an AI marketing workshop?
The primary measurement framework is cost per output unit and output volume per team member. Before the workshop, the team measures the current cost and time to produce a piece of content, a campaign brief, or a set of ad variants. After the workshop and a 60-day implementation period, those same metrics are measured again. Secondary metrics include the number of campaign variants tested per month, the speed from brief to live campaign, and the change in performance metrics for the specific channels where AI was applied. A review session at 90 days reviews all of these metrics against the baselines established before the workshop.
Ready to build an AI marketing workflow that produces commercial results?
Send a workshop inquiry with your team size, the AI tools you are currently using, and the marketing workflow you most want to improve. A brief call and pre-work brief will follow within 48 hours.
Book a speaking inquiry →