AI readiness report – Scale

Developing AI projects has many challenges, regardless of project size or industry. These challenges are within the whole AI lifecycle. If solved, the changes could yield better returns. Scale has produced its first state of AI readiness report for 2022, which outlines the challenges and solutions.

The research behind the report was based on 1,300 ML professionals across different company sizes or industry sectors. Most of the information looked at the issues and highlighted several solutions.

Key points

• 31% – said that data collection is an issue
• 33% – said that data quality is the difficult part of acquiring data
• 37% –  said they do not have a variety of data
• 67% – said that data noise was a problem
• 47% – say that data bias was a problem
• 47% – said that domain gaps (knowledge) were a problem
• 9% – indicated that their data was free from noise
• 30% – said that curating data was an issue
• 33% –  said that annotation quality was an issue
• 81% – said that their ML teams are somewhat or closely integrated with their annotation partners
• 73%  – used synthetic data for their projects
• 51% – used synthetic data because of insufficient real-world data
• 29% – used synthetic data to address privacy or legal issues
• 28% – used synthetic data to reduce long lead times
• 80% – evaluated model performance
• 56% – used aggregated model metrics
• 51% – look at evaluating business impact
• 40% – of smaller companies (500 employees) look at the impact on business
• 61% – of large companies (10,000 employees) look at the business impact
• 38% – said deploying was the most challenging part
• 34% – said monitoring was difficult
• 30% – said optimising computing was difficult
• Lots more…

Key takeaway and insights
The AI readiness report has pointed out many issues that will help improve future AI/ML development. One glaring issue is the low figure of companies measuring the impact of their models on their enterprises. It is good to have clear objectives and measurables regardless of business size. One common thread in this report is the issue of data and how the correct amount, quality and management are significant for downstream ML success.

What is needed:

• Have clear objectives for your AI and measurable benefits for business
• Concentrate on data quality, issues and volume
• Speeding up of curating data will help speed up deployment
• Importance of domain knowledge for labelling data
• Need for good data augmentation with fast model development, iterations and product testing

Great report that raises some critical issues. I look forward to seeing a change next year.

Please read the report here (They may need your details)

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