A Practical, Non-Hype List of AI Resources

Written By Kat Knocker

There’s no shortage of AI content out there. What is scarce are resources that are practical, trustworthy, and genuinely useful for people who don’t want to become deeply embroiled in technicalities.

Below is a curated list of AI resources I’ve found genuinely helpful, especially for non-technical professionals and leaders. It also includes some courses too. This isn’t exhaustive, and it’s not sponsored. It’s what I’ve been sending to SME and startup clients and friends recently when they’ve asked me “Where should I start?”.

I’ve sent it out so often I’ve made it into a page here. Any I’ve missed and you love? Do email me and I’ll get them added.

Thoughtful, Accessible AI Voices

If you want clarity without hype, these are excellent follows:

  • How to AI
    Clear, practical guidance with a strong focus on real-world use.

  • Heather Murray
    Brilliant at translating AI for non-technical audiences. Her newsletter AI for Non-Techies is especially approachable, and her post on “6 ways to use AI” is a great starting point. She also runs courses and memberships.

  • Luiza Jarovsky
    A must-read if you care about AI governance, law, and ethics. Her newsletter is an excellent grounding in what’s actually happening beyond the hype, and she does a course too.

  • Ruben Hassid

    Shares practical tips that actually work and can be actioned for all the major tools.

  • Ethan Mollick
    One of the clearest thinkers on how AI changes work, learning, and organisations. His book, Co-Intelligence, is also an excellent primer

  • Allie K. Miller
    Strong practical insights, a great newsletter, and also runs an AI Fast Track 5-day course.

  • Steve Cunningham / Simple Academy
    Business focussed resources with an ROI lens, making AI usable using workflows based on team structures and roles

Structured Learning (Courses & Learning Hubs)

From informal join-any-time resources to full in university courses, here are the ones that piqued my interest. Note, the university courses do vary year by year and there are more specialist ones available too to deeper dive on in many topics including Healthcare as an example.

Tools for experimenting

These tools could be used for experimentation, learning, and rapid prototyping, particularly in hackdays or personal exploration. They are not automatically appropriate for sensitive data or commercial deployment without careful review. You’ll know the major LLMs so I’ll not list those.

  • LaunchLemonade
    Fast, flexible, and powerful (oh and pretty cheap) for experimentation with AI workflows and agents. (Massive P.S. Last time we checked, their privacy policy wouldn’t be suitable for much beyond exploration for something fun like a hackday or a personal side experimentation project)

  • Lovable
    Great for no-code/“vibe coding” and quickly turning ideas into working prototypes. (P.s. consider that it’s great to get you started but really get into the details if you’re planning on using for real deployments. Things to consider include how easy to fix bugs, how you might do further building and also privacy implications and risks)

  • Bubble
    A long-standing no-code platform that’s useful for prototyping products and workflows. (Same considerations as per Lovable)

  • The other usuals
    familiar tools like Miro now use AI to help teams brainstorm, cluster ideas, and summarise workshops, while Figma uses AI to support early design exploration, generate layouts, and speed up iteration. Zapier enables experimentation with AI-driven automation between applications you’re likely to be using already.

Ethical and responsible AI use

Responsible AI isn't just about doing the right thing - it's about building trust with customers and teams as well as meeting regulatory requirements and avoiding costly mistakes. These resources are practical and relevant for smaller teams building AI into products, operations, or customer experiences.

  • ICO – Guidance on AI and Data Protection
    Essential for any UK startup using AI. Practical guidance on GDPR compliance, transparency, and accountability - critical for both FMCG and software applications.

  • Ada Lovelace Institute
    UK-based independent research institute examining data and AI. Their work on algorithmic accountability and public trust is especially relevant for those with consumer-facing products.

  • Algorithm Watch
    European-focused watchdog examining algorithmic decision-making. Valuable for understanding EU AI Act implications and what's coming down the regulatory pipeline for UK businesses and a thoughtful blog covering broad topics.

  • Montreal AI Ethics Institute
    Clear, actionable resources including practical guides and a weekly newsletter. Perfect for smaller teams who need to implement ethical AI without large dedicated ethics functions.

  • Luiza Jarovsky
    A repeat from the list above but this definitely slots in here too. Her newsletter is an excellent grounding and is always up to date. Useful for startups thinking about long-term impact and risk management.

  • AI Safety Movement

    A thoughtful and well-researched Report that showcases a number of areas to bear in mind with AI adoption and longer term themes.

AI Resources for Boards & Non-Executive Directors

For boards, the goal isn’t to become AI experts, but to ensure the organisation is asking the right questions and putting appropriate governance in place as AI raises questions of strategy, risk, ethics, accountability, and regulatory exposure. The resources below are designed specifically for board-level viewpoints rather than tools per se.

  • Institute of Directors (IoD) – AI and the Board

    Practical UK-focused guidance outlining what boards should be asking about AI, including risk management, governance structures, and decision accountability. A strong starting point for board discussions.

  • World Economic Forum – AI Governance & Board Oversight

    Globally recognised frameworks and papers on responsible AI, organisational readiness, and oversight models often referenced by regulators and large enterprises.

  • OECD – AI Principles

    Internationally adopted principles for trustworthy AI. Useful as a defensible baseline for board policies and risk frameworks.

  • Alan Turing Institute – Ethics & Responsible AI

    UK research and practical guidance on ethical AI, bias, and societal impact — helpful for boards thinking about long-term risk and reputation.

    Conferences

    A slim list of the broader conferences to get you started, there are many more popping up all the time and those that are more specialist too.

  • Create With
    A practical, creative forum focused on how people are actually building with AI, with an emphasis on experimentation, and community. Often focused on small or bootstrapped companies. 2026 date: 25th June

  • AI and Big Data Expo
    A large, commercially focused event bringing together vendors, enterprises, and leaders to explore applied AI use cases, strategy, and adoption at scale. Free tickets to premium tickets available. 2026 date: 4-5th Feb

  • AI Summit London

    Part of London Tech Week, A major UK AI event featuring industry speakers and case studies and lots of solution providers to showcase real-world applications and insights. Free tickets available with limited access, plus premium with broader access. 2026 date: 8-12 June

  • AI UK (The Alan Turing Institute) – The UK’s national showcase of data science and AI research, policy, and real-world applications, with talks, demos and networking in a multidisciplinary context (so, think researchers, students and businesses present). 2026 date: TBC

  • Mindstone meetups

    Community-led meetups focused on practical AI literacy, peer learning, and real-world experimentation. London and other locations.

    How to move forward

    I hope this list gives you a clear place to start and lightens the load of navigating forward wherever you are in the AI maze.

    It’s great to start by encouraging experimentation. Focus on use cases where you have a clear hunch about impact, rather than trying every new tool and AI in every team. At the same time, protect your organisation’s data, intellectual property, and people by using properly licensed tools. Take time to understand how data is handled (and make sure that’s OK) and set clear boundaries with your team on what should and should not be shared with AI systems.

    Effective progress with AI will come from balance: an open mind and willingness to experiment, grounded in education across the organisation; a shared vision and philosophy set by leaders; and a clear focus on return on investment. All of this needs to sit within appropriate governance, including sustainability and long-term impact. Used this way, AI becomes not a race to adopt tools, but a deliberate capability that supports better decisions and lasting value.

    If you want to chat AI some more, please head on over to my contact page.

Kat Knocker