AI workshops

We begin by understanding your unique position as life sciences marketers. What’s your current relationship with AI? Are team members experimenting quietly, or is there organisational hesitation? What’s driving the interest – time pressures, budget constraints or competitive pressures?

More importantly, we explore the constraints that generic AI training ignores. What level of scientific accuracy do you need to maintain? How do you currently maintain it? What are your regulatory considerations? How technical is your audience? What happens if you publish something that’s scientifically incorrect?

We also assess your content landscape: which marketing assets require deep scientific expertise (and shouldn’t be delegated to AI), and where could AI genuinely free up valuable time? This isn’t a one-size-fits-all session – it’s tailored to your team’s technical capabilities, risk tolerance and business objectives.

Here’s where we diverge from standard AI workshops. Rather than theoretical discussions about the ‘future of marketing’ or broad ‘use cases’, we get immediately practical with our Scientific Marketing Task Audit.

We guide your team through a systematic review of actual work – everything from weekly social posts and monthly blog articles to quarterly campaign planning and annual strategy reviews. For each activity, we evaluate:

Frequency – how often does this activity occur?
Time investment – how many hours does it consume?
Business value – how critical is this to achieving your marketing objectives?
Technical complexity – how much scientific expertise is required?
AI suitability – could AI assist without compromising accuracy or brand voice?

This creates a personalised priority matrix that reveals where AI can add the most value with the least risk. Unlike generic frameworks, ours explicitly accounts for scientific accuracy requirements – a white paper on proteomics demands different treatment than a LinkedIn post about a product launch.

The output? A clear, ranked list of opportunities specific to your team and your scientific field.

This is where theory meets reality. We introduce your team to AI tools that actually work for technical marketing, with live demonstrations and guided practices.

Life sciences AI tool stack

We don’t just showcase the obvious (ChatGPT, Claude, Gemini). We introduce the full stack of tools that scientific marketers need:

Frontier models – which one handles technical accuracy best for different tasks?
Research tools – AI for literature reviews, competitor intelligence and scientific insights
Content tools – purpose-built for repurposing technical content, not creating it from scratch
Design tools – for creating scientific infographics, data visualisations and presentation slides
Workflow automation – connecting AI to your existing tech stack (CRM, marketing automation, project management)

Scientific prompt engineering

Generic “ChatGPT prompts for marketers” don’t work when you’re discussing CRISPR, mass spectrometry or monoclonal antibodies. We teach prompt frameworks specifically for scientific content:

Context setting – how to prime AI with the right scientific background
Accuracy constraints – how to request citations, specify terminology and maintain technical rigour
Output structuring – how to get results formatted for scientific audiences
Iterative refinement – how to improve AI outputs through follow-up prompts

Participants work through real scenarios from their own marketing calendars – not generic examples – so they leave with immediately applicable skills.

Technical accuracy workflow

We introduce our proprietary approach: the scientist-AI-scientist workflow for scientific marketing. This isn’t just about reviewing AI output – it’s a systematic process that ensures every piece of content maintains scientific integrity:

1. Human expertise first – define the technical requirements, key messages and scientific accuracy standards
2. AI augmentation – use AI to accelerate drafting, research or formatting
3. Human validation – review for scientific accuracy, technical precision and brand alignment

This is where kdm’s PhD-level team becomes your safety net (more on this below).

At the end of the workshop, we will help your team create a practical 90-day implementation plan. This includes:

Priority initiatives – ranked by impact and feasibility, based on your Scientific Marketing Task Audit
Tool recommendations – specific AI platforms matched to your team’s needs and budget
Workflow integration – how to embed AI into existing processes without disruption
Risk mitigation – clear guidelines on what content types require human-led creation vs. AI assistance
Success metrics – how to measure time saved, quality maintained and ROI achieved

Hero Content Framework

A critical part of your roadmap is our Hero Content Framework – explicit guidance on when to use AI and when not to:

Human-led (hero content):

White papers, application notes, technical guides
Case studies with customer data or clinical outcomes
Thought leadership articles and bylined content
Scientific presentations and webinars
Regulatory- or compliance-sensitive materials

AI-assisted (secondary content):

Social media posts and captions
Blog articles (with human review)
Email marketing copy
Content repurposing and reformatting
Campaign brainstorming and ideation

This framework protects your credibility while accelerating lower-stakes content production.

Introducing VaaS: validation as a service

Here’s what sets kdm apart from other AI training providers. We don’t just teach you to use AI – we offer ongoing VaaS.

As a specialist scientific agency with PhD-level writers across multiple disciplines, we can act as your technical validation layer. When you create AI-assisted content, our team can:

Audit for scientific accuracy – check terminology, methodology and claims
Verify technical precision – ensure complex concepts are explained correctly
Review regulatory implications – flag potential compliance issues
Maintain brand consistency – ensure AI output aligns with your established voice

Think of it as an insurance policy for your AI-generated content. You get the efficiency gains without the risk of publishing something scientifically inaccurate or off-brand.

VaaS can be purchased as a monthly retainer or on a project-by-project basis, giving you flexible access to scientific expertise whenever you need it.