# Ethereum Scaling

**Published:** 2026-07-22T17:54:37.935Z  
**Topic:** Layer 2 Scaling  
**Sentiment:** neutral  
**Publisher:** TrendWatcher — https://www.trendwatcher.in/article/1b9ab715-4076-4497-a5e0-d27d91667141

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

1. Ethereum's scaling challenges are a major concern for engineers, with the platform's ability to handle large-scale AI systems being a key issue [2]. The stake is high, with companies like Brickworks using AI to improve data quality, but struggling to deploy it consistently across the enterprise [3].

| 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 |

2. The body of the issue is complex, with multiple factors at play. According to a report by Boston Consulting Group, successful AI scaling depends more on organisational capabilities such as governance, data foundations, operating models, and workforce readiness than on deploying more sophisticated models [3]. This is evident in the case of Brickworks, which is using AI to improve data quality, but still relies on establishing clear data ownership and governance across the business [3].

## What drove the move
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
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 |

## What to watch
* The development of governance and workforce readiness in companies like Brickworks
* The investment in data and analytics foundations, with companies investing up to 4 times more in this area
* The deployment of AI consistently across the enterprise, with only 5% of companies achieving AI value at scale

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.

## Sources
1. NewsBytes — [Model Context Protocol update next week simplifies enterprise AI scaling](https://www.newsbytesapp.com/news/science/model-context-protocol-update-next-week-simplifies-enterprise-ai-scaling/tldr)
2. Hackernoon — [Why do engineers worry about scaling? | HackerNoon](https://hackernoon.com/why-do-engineers-worry-about-scaling-49877739ba0d)
3. iTnews — [State of Data & AI 2026: Scaling AI](https://www.itnews.com.au/state-of-data-ai-2026/state-of-data-ai-2026-scaling-ai-627385)

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Cite as: TrendWatcher, "Ethereum Scaling", https://www.trendwatcher.in/article/1b9ab715-4076-4497-a5e0-d27d91667141