You can’t predict the future, but you can build the fitness to respond to it. The advantage isn’t prediction. It’s how fast your leadership team can notice and anticipate change and respond. Hence mind candy — an information stream to help you and your team think about what’s next.
‘Garbage in, chaos out’: overcoming the curse of bad AI data
Because AI is trained on curated datasets, it can only ever be as good as that data. It doesn’t matter what AI model is being used, or who is operating it – if the data is poor, whether messy or missing key detail, then the output won’t be reliable.
The leadership question
You have to assume that your AI has food poisoning. How good is your data, really?
The other prompts:
My latests book about books about AI
Data, acceleration, and the future of intelligence: Lessons from 25 core books about AI, technology abstraction, and consciousness. Also available as a board briefing.
https://www.ronimmink.com/product/a-book-about-books-about-ai/
PwC’s AI agents are now your consultants — whether you’re ready or not
PwC’s new agentic platform cuts consultants out of the loop as first responders. What that means for pricing and liability remains an open question.
https://thenewstack.io/pwcs-ai-agents-are-now-your-consultants-whether-youre-ready-or-not/
How ERP Data Fits Into The Enterprise Data Ecosystem
ERP is no longer the single center of gravity for enterprise data. It has become one of several anchor systems in a broader data ecosystem, and that shift now requires intentional architectural decisions that many enterprises have not yet made.
Low code, no fear
How can CIOs address the security accountability of low-code developments so that it does not become a showstopper for democratization of innovation at speed and scale?
https://www.cio.com/article/4149388/low-code-no-fear.html
Beyond language: Why world models could be the next frontier for enterprise AI
Joint Embedding Predictive Architecture is a learning method he proposed in 2022 that trains AI systems to develop abstract representations of their environment rather than generating outputs word by word.
https://www.ibm.com/think/news/world-models-next-frontier-enterprise-ai
Digital Transformation Fatigue: When the Future Becomes a Full-Time Job
By framing transformation as a manageable, ongoing effort rather than a magic fix, companies can reduce fatigue and focus on meaningful progress in managing their enterprise content and data.
https://formtek.com/blog/digital-transformation-fatigue-when-the-future-becomes-a-full-time-job/
‘I am not a fan of AI’: Apple’s co-founder slams artificial intelligence, saying it lacks human emotional depth
Architecting for Data in Motion: Gone Are the Days of Data at Rest
The concept of “data in motion” is transforming the way organizations think about their technology stack and will determine which organizations can actually execute on AI and which are left drowning in endless streams of data.
https://www.rtinsights.com/architecting-for-data-in-motion-gone-are-the-days-of-data-at-rest/
Data science in the age of AI: From experimentation to scalable, governed systems
As experimentation becomes easier, the real challenge is no longer whether teams can build models, but whether those models can scale, be trusted, and support real-world decision-making.
https://www.eweek.com/artificial-intelligence/ai-data-science-workflows-cloud-scale/
The autonomy trap: Why AI agents need strict boundaries to deliver real value
The primary question is no longer whether such systems can be built, but whether they can operate legitimately within enterprise governance and accountability structures.
The autonomy trap: Why AI agents need strict boundaries to deliver real value
