Helping business leaders build and run AI roadmaps for growth. | O'Reilly Author | LinkedIn Learning Instructor | Keynote Speaker | Managing Partner RAPYD.AI
Stop looking for AI Use Cases Here's a scene I witness too often: A company decides they need to "do AI". They bring in expensive consultants, form innovation teams, and start listing every possible place they could use AI. Each workshop generates dozens of "promising use cases". Six months and €200,000 later, they have beautiful PPT decks but zero impact on their P&L. Or even worse, they decided to implement "AI Agents" in all their workflows because Google said so. The only certain outcome of this is that, you'll kill your AI journey faster than you can say "experimentation". You need to approach your AI initiatives from the other way around. In my newsletter tomorrow, I'll show you a way to skip sourcing pointless AI projects and getting your first $10K additional profit with AI instead. Subscribe here before 12pm Friday so you don't miss it: [link under my name]
Just goddam build stuff from the ground up
Pretty much how organizations should think about their initiatives. The models, platforms & infra can be rolled into a single system of intelligence and the applications / agents on top of it - you need a different way to now measure impact though of these - traditional analytics don't cut it.
Have to disagree. Most people have no idea what AI can do. You need to experiment. Working backwards from your problem will end up with you “waiting” for everyone else to figure it out first.
You said it well the other day in your blog post. Make a simple spreadsheet. Collect 10 - 15 company uses cases. Build prototypes. Reiterate.
Agents everywhere :-) Isn't it time to look beyond the GenAI hype and put the spotlight on really important issues such as minimizing material and energy consumption and increasing production capacity through green innovations?
Don't chase AI trends, let AI chase your goals.
I just subscribed : this will probably be the only good news of the week! 😂
Absolutely agree. The most effective AI initiatives come from identifying tangible challenges within the company - challenges that should primarily be identified internally rather than imposed by external consultants. Starting with a focus on specific, measurable outcomes - whether in value creation or efficiency gains - is essential. It’s also crucial to evaluate implementation and adoption costs early on to ensure that each AI project not only makes strategic sense but also contributes positively to the bottom line. Looking forward to the insights in your newsletter!
AI Native | AI Consultant | AI Systems Engineer | Cybersecurity Expert
1wStart small-> Iterate-> Bring ROI-> Expand. Internal AI projects aren`t exceptions. If you start to make money, business will be your best ally. Ironically, many businesses will hesitate to spend $10K on a small, practical AI project but will readily spend $100K on consulting presentations that might never translate to real value.