Global investment in AI is growing at a dizzying pace – by 2026, it is expected to reach as much as $480 billion. Companies around the world are counting on AI to provide them with a quick return on investment, greater operational efficiency and a competitive advantage in a rapidly changing market. However, the reality can be brutal – as many as 85% of AI projects fail, with poor, inconsistent or incomplete data being the most common cause of failure.
Key takeaways from this article
- As much as $408 billion is invested in projects that will never generate real business value.
- Low data quality drives a technology gap that hinders AI implementation, increases project costs, and delays business results.
- As many as 85% of AI projects fail – most often due to inconsistent, incomplete, or outdated data.
- Britenet’s Data Health Assessment is the first step towards real return on investment and avoiding technological debt.
Why is investing in AI not profitable?
There will be no return on investment if the data on which the AI algorithms are based is inconsistent, incomplete and outdated – according to a Qlik study, as many as 81% of organisations have problems with data quality1, and one in three leaders does not trust AI-generated analyses and reports, primarily due to the questionable quality of the input data.
Poor data quality in an organisation translates into real financial and operational losses. Inconsistent data leads to wrong decisions, duplication of work and wasted budgets on for instance ineffective marketing activities. It also poses a risk of financial and reputational losses associated with non-compliance with regulations. Low-quality data affects every stage of an AI project – from proof of concept (POC) to model training and implementation.
Data quality – costly mistakes that can be avoided
Although data is the fuel for modern solutions, it is still neglected by most companies and rarely seen as a strategic resource for the organisation. Without prioritising its quality, organisations not only risk AI project failure, but also expose themselves to costs associated with non-compliance with regulations such as the Data Act or GDPR.
- 38% of companies do not trust their own data.
- 43% of companies doubt the reliability of AI solutions.
- 45% of companies have problems with the responsible use of AI.
- 81% of companies do not treat data as a priority.
- 96% of data specialists warn of a data quality crisis.
If 85% of all AI projects fail, this means that as much as $408 billion is invested in projects that will never deliver real business value. Poor-quality data is largely to blame for this.
Let us take care of your data and help accelerate growth with data potential analysis
Britenet’s Data Health Assessment is a quick, targeted analysis that helps assess the readiness of data for use in AI projects, automation, and cloud solution implementation. It is a specific, measurable study that helps determine whether the data in an organisation is complete, consistent, useful, and ready to generate profit.
A comprehensive data health assessment allows you to make better budget decisions, avoid increasing technological debt, and accelerate the time to value of your investment.
To support your digital transformation efforts, we’ve created an organisational readiness assessment. After completing it, you’ll receive a personalised action plan outlining your current strengths and pinpointing key areas for improvement across six critical dimensions.
1https://www.qlik.com/us/news/company/press-room/press-releases/data-quality-is-not-being-prioritized-on-ai-projects
