Stop Buying AI Hype. Start Building Capability.

1. AI Without Data = Failure

AI is hungry. It consumes data to learn, predict, and generate. But if the data is incomplete, inconsistent, or untrustworthy, the outputs fail — fast and at scale.

AI projects fail at nearly twice the rate of non-AI projects, with poor data quality and unclear value as the root causes (RAND Corporation, 2023).

What this looks like in practice:

• Predictive models misfiring because product, customer, or operational data isn’t consistent.
• Generative AI hallucinating because it has no high-quality ground truth to draw from.
• Costs ballooning as project teams spend more time fixing data than delivering outcomes.

No data, no AI.

2. Data Without People = Waste

Every enterprise has oceans of data. Warehouses. Lakes. Dashboards. Reports. But none of it delivers value until people know how to use it in their flow of work.

Only 27% of employees globally feel confident using data to make decisions (Accenture, 2022).

The consequence:

• Decisions default to instinct or hierarchy.
• Valuable insights sit idle in unused dashboards.
• Opportunities to act in real-time are lost.

The shift literacy creates:

• People move from “What does this mean?” to “Here’s what we do next.”
• Teams stop debating definitions and start aligning on actions.
• Everyone — from frontline to C-suite — understands their impact and value in the end-to-end flow.

No people, no value.

3. People Make the Difference

When humans are confident and literate, they turn AI from hype into outcomes.

Organisations with strong data literacy are 3× more likely to outperform peers on revenue growth (Qlik & Accenture, 2022).

Firms that combine AI with data maturity and human capability achieve 2–5× ROI compared to tech-only approaches (McKinsey, 2023).

This is the differentiator:

• People who can question data, trust it when it’s right, and act on it with confidence.
• Leaders who use evidence to frame better questions and make sharper calls.
• Teams who embed AI as a tool in their everyday decisions, not a shiny experiment on the side.

The differentiator isn’t the model. It’s the people who know how to use it.

From Hype to Capability

The hype paints AI as autonomous intelligence. The reality is simpler — and far more useful:

• Not machines replacing humans → but humans using better tools.
• Not platforms as the prize → but capability as the multiplier.

Think about it:

• A hammer never built a house.
• Excel never made a decision.
• AI won’t either.

It’s the people in the system — confident, literate, and connected — who convert AI + Data into performance.

No data, no AI. No people, no value.

👉 Stop buying the AI hype. Start building the capability.

That’s where XOAM comes in. We partner with leaders to:

• Cut through complexity.
• Embed Data & AI into the flow of work.
• Build people capability at scale.

What Organisations Receive

• A baseline to know where you stand.
• An index to measure progress.
• A design to shape what’s next.
• Delivery that makes it real in the flow of work.

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_References

_{1. RAND Corporation (2023). Why AI Projects Fail. https://www.rand.org/pubs/research_reports/RRA2671-1.html}

_{2. Accenture (2022). Closing the Data Literacy Gap. https://www.accenture.com/content/dam/accenture/final/accenture-com/document-2/Accenture-Ex-Client-Brief-Final.pdf}

_{3. Qlik & Accenture (2022). Data Literacy Index. https://thedataliteracyproject.org/wp-content/uploads/2022/11/The-Seven-Principles-of-Data-Literacy-by-Qlik-and-Accenture.pdf}

_{4. McKinsey & Company (2023). The State of AI in 2023: Generative AI’s Breakout Year. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year}

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