You're nowhere near ready for AI! Here's what you need to get right first скачать в хорошем качестве

You're nowhere near ready for AI! Here's what you need to get right first

00:00 - You're nowhere near ready for AI! Here's what you need to get right first 08:33 - Dan starts the “AI readiness” section: why most teams want AI tools before they are actually ready 09:19 - The “iceberg” idea: most AI work is hidden (governance, process mapping, change management, clean architecture) 10:10 - Why AI projects fail: unsexy fundamentals get skipped, lack of purpose beats lack of tech 10:35 - “Shiny object syndrome”: seeing flashy demos and assuming plug-and-play will work internally 11:22 - Data governance point: messy data makes AI hallucinate more confidently 11:33 - Process mapping point: you cannot automate a process you do not understand 11:43 - Clean architecture point: AI must connect to existing systems, not sit on top of chaos 13:01 - The people layer: purpose, data quality, and cultural resistance as the real blockers 14:05 - “AI is an accelerator”: it speeds you up in whatever direction you are already going 15:03 - Practical example: chatbots can amplify bad customer experience instead of fixing it 16:27 - “Data swamp vs data lake”: AI will produce polished output even when underlying data is wrong 16:49 - First concrete step: run a data audit and identify what is inconsistent, biased, or missing 17:02 - Silo problem: teams optimize their own KPIs instead of one shared business goal 18:02 - Context gap: AI spots patterns but needs human context to explain “why” behind changes 19:00 - “Human compass” framework: humans supply ethics, reality checks, nuance, and judgment 20:25 - The “should filter”: AI asks “can we,” humans must ask “should we” 21:23 - “Data needs meaning”: having data is useless if the org cannot translate it into decisions 23:10 - How to clean data: personas, cross-platform stitching, relevance over “collect everything” 25:24 - Segmentation depth: LTV by channel, device, time, and cohort is what makes AI useful later 28:39 - AI readiness roadmap: moving from descriptive to diagnostic to predictive to prescriptive to preemptive 32:54 - How to use the roadmap: start where you are, move step-by-step, do not jump to “AI magic” 35:19 - Why AI cannot “skip levels”: you must understand KPIs and goals yourself or you automate confusion 37:12 - Reframing AI: treat it like an intern (needs oversight), not a manager (trusted blindly) 38:05 - People readiness: upskill vs replace, create sandbox experimentation, build AI literacy in teams 39:12 - Governance and safety nets: liability, privacy, audit trail, and “show your working” principles 42:53 - Broken process warning: “automated chaos is still chaos,” fix workflows before scaling with AI 44:43 - Readiness checklist: data accuracy speed, ability to map core process, and staff mindset (excited vs threatened) 46:40 - Competitive advantage shift: AI makes efficiency baseline, human insight and behavior become differentiation 48:16 - “Thick data vs big data”: quality insight behind the click is what keeps content and strategy human 49:36 - Earn AI: centralize and clean data, repair processes, add guardrails, build psychological safety 52:17 - Closing principle: use AI to be more human, then reinvest time into empathy, insight, and relationships 54:10 - First steps to take home: audit one process, clean one dataset, and have an honest team conversation about fears Everyone is rushing to implement AI, terrified of being left behind. But here is the uncomfortable truth: AI is a multiplier, not a magic wand. If you apply it to bad data, broken processes, or a disorganised team, you won't get innovation—you’ll just get bad decisions, faster. In this talk, we are pressing pause on the hype. I will walk you through the unsexy but critical foundations you must build before spending a penny on AI tools. From cleaning up your data architecture to upskilling your workforce, discover the roadmap to getting your house in order so that when you finally do pull the trigger on AI, it actually works. ====*****=====*****===== Dan Saunders, Head of Performance Marketing at Evergreen Finance For 18 years, I have been fueled by an obsession with the intersection of data and digital performance. I help businesses unlock new opportunities by turning raw insights into robust E-commerce and marketing strategies. Whether I’m leading large teams, managing multi-channel campaigns, or speaking on international stages, my goal is the same: to empower organisations to embrace data-driven decision-making. I thrive on demystifying digital complexities for leaders and building the capabilities required to dominate the market. ====*****=====*****===== Find out more and register: https://www.seocharity.com/upcoming See our upcoming conference: https://www.seocharity.com/upcoming-c... If you want to donate: https://donate.stripe.com/6oE15C1Srfz... #seocharity