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Ethereum faces scaling challenges with only 5% of companies achieving AI value at scale, investing up to 4 times more in data and analytics foundations, with a
| At a glance | |
|---|---|
| AI adoption rate | 84% of software developers use or plan to use AI tools |
| AI scaling success rate | 5% of companies achieve AI value at scale |
| Investment in data and analytics | Up to 4 times more than other areas |
| Key challenge | Governance and workforce readiness |
The move towards scaling AI is driven by the need to deploy it consistently across the enterprise, with companies like Brickworks using AI to improve data quality and accelerate the process of data cleansing [3]. However, the challenge of scaling AI is not just technical, but also organisational, with companies needing to invest in data and analytics foundations, governance, and workforce readiness [3].
The competitive picture is complex, with companies like Brickworks using AI to improve data quality, but still facing challenges in deploying it consistently across the enterprise [3]. According to a report by Forrester, only a small minority of companies are running agentic AI in meaningful production, with true scaled multi-agent systems proving rare [3].
| Company | AI adoption rate |
|---|---|
| Brickworks | Using AI to improve data quality |
| Software developers | 84% use or plan to use AI tools |
The real significance of Ethereum's scaling challenges lies in the need for companies to invest in organisational capabilities such as governance, data foundations, operating models, and workforce readiness, rather than just deploying more sophisticated models [3]. The open question is whether companies like Brickworks can successfully deploy AI consistently across the enterprise, and achieve AI value at scale.
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AI-assisted synthesis by the TrendWatcher Editorial Desk · sourced from 3 outlets · Jul 22, 2026 · How we report
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