AI Training for Startups & Scale-ups

A working model of what generative AI does well, where it fails, and why those limits matter for a company still testing its product, market, and operating model. Teams learn to tell genuine acceleration apart from output that only looks finished.

• What a language model is doing under the hood
• What to hand to AI, and what a founder keeps
• Confident, wrong answers: hallucination and stale information
• Testing an idea without treating an answer as proof
• Ranking ideas by learning value and effort, with risk checked

Repeatable ways to brief AI for the research, writing, and synthesis that fill a startup week, with a source check and a human edit before anything goes out.

• Meeting briefs, decision memos, investor updates
• Turning scattered notes into one summary
• What options look like
• Giving context without leaking confidential detail
• One prompt template, shared by the whole team

Good discovery work uses AI to organize evidence and pressure-test an idea, never to invent certainty about a user who was never in the room. Participants synthesize interview notes and draft the actual product artifacts. They build a prototype good enough to test one question. Anything that survives testing crosses into production through the engineering team's normal review.

• Turning interview notes into clear themes
• Hypotheses and experiment briefs, with acceptance criteria attached
• Comparing evidence without making up a customer
• AI as a critic
• One prototype, one question
• Handing off a validated prototype with context

Commercial teams use AI to prepare, personalize, and learn faster from customer-facing work, while claims, brand voice, and relationship judgment stay with people. The exercises run from account research through to feedback synthesis.

• Account research and discovery questions, sourced properly
• Outreach drafts a human still reads first
• One campaign idea, several channel drafts
• Sorting objections and win-loss notes into themes
• Checking claims and tone, including brand voice, before publishing
• Feeding what customers say back into the roadmap

Teams map the recurring handoffs in hiring, onboarding, finance admin, reporting, and internal knowledge. Then they decide whether a prompt, a template, an off-the-shelf tool, an integration, or a custom build is the smallest thing that will hold up.

• Mapping triggers and owners, plus failure points
• Better onboarding and docs, alongside routine coordination
• When a template is honestly enough
• Weighing embedded and specialist options, including custom builds
• Counting the maintenance and switching cost, honestly
• One automation, one human checkpoint, one fallback

The final module turns individual experiments into something the whole team can run. Participants set data and IP boundaries, name owners and review points, and leave with a staged plan for pilots that can be stopped, improved, or expanded as headcount grows.

• Sorting public, internal, customer, and IP-sensitive data
• Setting tool, access, retention, and review boundaries
• What counts as reviewed, sourced, customer-facing output
• Picking pilots with a named owner
• A lightweight review cadence across the business
• The 90-day call: stop or continue, then decide whether to scale