Impact of AI Innovations in Enterprise Context

AI innovations continue to deliver huge benefits to businesses, and adoption rates will accelerate in the coming years with significant impacts on businesses.

FREMONT, CA:AI techniques have evolved beyond the regular AI tasks used to add intelligence to static business applications, devices, and productivity tools in recent studies. Businesses should focus on innovations expected to become mainstream adoptions in the coming years, including composite AI, decision intelligence, and edge AI. Early adoption of these innovations can propel significant competitive advantage and business value and ease problems associated with AI models’ fragility.

The wide range of AI innovations will impact people and processes within and outside an organisation, making them important to understand for many stakeholders, from business leaders to the enterprise engineering teams that started deploying and operationalising AI systems. Data and analytics (D&A) leaders have the most to gain, and AI strategies for the future and using technologies will have a high impact in the present. AI innovations fall into four major categories, such as data-centric AI, model-centric AI, application-centric AI, and human-centric AI.

The AI community has emphasised enhancing outcomes from AI solutions by tweaking the AI models, but data-centric AI shifts the focus toward improving and enriching the data used to train the algorithms. In addressing AI-specific data considerations, data-centric AI disrupts traditional data management, but companies investing in AI at scale will evolve to preserve conventional data-management practices and extend them to AI in different ways. This includes adding the capabilities necessary for convenient AI development by an AI-focused audience that is unfamiliar with data management. Along with this, there is the use of AI to improve and increase the classics of data governance, persistence, integration, and data quality. Data-centric AI innovations include synthetic data, data labelling, knowledge graphs, and annotation.

Synthetic data is an artificially generated data class rather than obtained from direct observations of the real world. Data can be generated using different methods, including statistically rigorous sampling from real data, semantic approaches, generative adversarial networks, or by creating simulation scenarios where models and processes interact to build completely new datasets of events. Adoption is increasing across different industries, along with its use in computer vision and natural language applications. However, a massive increase in the adoption of synthetic data avoids using personally identifiable information when training machine learning (ML) models through synthetic variations of original data or synthetic replacement of parts of data. It also reduces cost and saves time in ML development as it is cheaper and faster to obtain, and it improves ML performance as more training data results in better training outcomes.