Closing the AI performance gap
This article shows a significant, widening gap between businesses who have embraced AI and those who have been slow to adopt it. From a survey of 1,018 executives, only 25% had fully embraced AI, while 55% were still experimenting. Businesses that had invested heavily in AI before the pandemic, regardless of size, were more likely to have experienced positive impacts during the crisis.
Early adopters are also more likely to continue expanding their AI capabilities, reaping greater benefits as a result. Meanwhile, companies that did not invest early continue to struggle with poor performance and limited funding for AI. To deploy AI models in operations and close this gap, the article recommends:
- Embedding leadership - those that fully embedded AI had done so across their business processes, many with at least 10 AI applications in deployment.
- Gaining scale to capture returns - businesses will need to invest in a range of capabilities to exploit the full potential of AI, from data engineers to tech ethicists.
Is your data governance AI ready?
As digital technologies such as AI power an ever-expanding portion of the enterprise world, data governance is becoming increasingly critical. High-quality data - managed as a global asset and leveraged as a key element of digital transformation, analytics, and insights - has become a competitive differentiator.
But the definition of data governance has not kept up with all the changes shaping the competitive landscape. While many organisations continue to use traditional approaches to data governance focused largely on processes, policies, and individual transactional data domains, key technology trends taking hold over the last few years have created the need for a major shift. Examples include:
- Cloud. Growing use of cloud technology often brings an increasing proportion of unstructured data, an expanded role for third-party service providers, and the need to shift from local to global policies.
- Agile. Agile methodologies are often at loggerheads with governance, which may be viewed as a constraint.
- Self-service analytics. With a growing need for up-to-the-minute business insights, companies are increasingly embracing self-service analytics. Enabling such capabilities requires a strong data governance foundation to support quality data and real-time insight delivery.
In order to survive and ensure their competitiveness into the future, organisations of all sizes must adapt and expand their data governance approaches accordingly.
Technology Vision report
The Technology Vision report is a systematic review across the enterprise landscape to identify emerging technology trends that will have the greatest impact on companies, government agencies and other organisations in the coming years.
The report is based on interviews with technology luminaries and industry experts, as well as a global survey of business and IT executives to understand their perspectives on the adoption and impact of technologies in their organisations.
The themes are weighed for their relevance to real-world business challenges and concentrate on items that will appear on the C-Suite agendas of most enterprises.