Maya HTT reposted this
Agree ! That's why at Maya HTT we don't accept to do any AI project with our customers before having an alignment with them to assess their AI readiness. - Availability of the data - Relevance of the data - Cleanliness of the data - Variance in the data. Following that alignment, we make sure to build the right foundation. This is key for any AI implementation project. DM for more details.
Transform your AI & data ambition into action | xQuantumBlack, xMcKinsey | Global top 100 Innovators in Data & Analytics ’24 | Founder & Director, Cambiq
Everyone wants AI magic. Few want to invest in the plumbing. We live in a moment where the promise of AI is louder than the reality of data. I see it every week with teams. The excitement is real. The budgets are flowing. The pilots look impressive. But when you lift the lid a different story shows up. Data that lives in twelve places. Metrics that mean one thing in finance and another in operations. Ownership that sounds like “we think it sits with them”. Governance that feels optional. And still we keep sprinting toward AI, hoping it will smooth over the cracks. It never does. It makes them more expensive. Here is the part no one markets: AI built on weak data creates more rework, more delays and more organisational friction than leaders expect. If you want real value, start with the boring foundation: → Clear definitions → Reliable data → Agreed owners → Confidence in the outputs When these are in place AI finally becomes what everyone wants it to be: simple scalable repeatable. So here is my question for every exec team: What is the data truth you have been quietly avoiding? ♻️ Repost to help someone get their data AI-ready. 🔔 Follow Clare Kitching for insights on unlocking value with data & AI.