Composite AI - the next stage in the evolution of AI
Artificial Intelligence (AI) is deployed for many different use cases in a variety of sectors, delivering untold business benefits to enterprises. AI can locate and manage data, provide actionable insights that allow organisations to manage and mitigate risks more effectively, better understand customer requirements, improve the sales pipeline and much more.
As any technology evolves, however, it can deliver even more tangible benefits as it does. That's what is happening with AI in 2023. Composite AI is a new approach that fuses different AI disciplines and techniques, adding user context to content and intent to deliver significantly more relevant and accurate search results. These results will then provide the foundation for a whole range of business-critical intelligent applications.
Composite AI - A Game Changer
Insight is of the highest importance in modern business. Without insight into the potential risks and threats facing an organisation, insight into service levels or customer requirements, any business will struggle to maintain its position. Decision-making relies on a deep understanding of a topic, so organisations must do everything they can to find and use such insight.
AI techniques have been a key technology in helping organisations locate and manage unstructured data, such as email correspondence, call transcripts, earnings announcements, news reports, social media and premium data sources, and then extract actionable insight from that data.
Typically, organisations utilise Machine Learning (ML) and Natural Language Processing (NLP) techniques to find the signals and then present those findings to the user. And ideally, these tools are integrated into existing systems and workflows. Most sales teams work with CRMs or customer service platforms such as Salesforce, for example.
ML is purely statistical. It may cover 90% of the information, but sometimes the right answer to a user's query is found within the 10% that is not covered. Furthermore, when ML provides you with a recommendation, it does not explain why – this context can be essential. Finally, it cannot model explicit domain knowledge into the search bar or insight engine. To improve this, Composite AI combines statistical AI with symbolic AI in a way that takes into account the representation of knowledge and the reasoning behind it.
Introducing Composite AI
In simple terms, a Composite AI platform generates insights from any content and data by fusing different AI technologies, such as machine learning and NLP with graph technologies. Gartner first coined it in 2020, stating that Composite AI refers to the different AI techniques used in combination to achieve the best possible result.
The combination of ML and NLP with knowledge graphs delivers faster and more relevant search and recommendation results, more accurate insights and a reduced time to value. It allows for completely new personalisation capabilities, displaying different search results depending on the function or business domain of the user in that specific context.
The addition of knowledge graphs means a user can explicitly model the domain knowledge in that field, significantly advancing what is achievable without Composite AI. Composite AI overcomes the identified barriers and shortcomings of ML to achieve markedly improved results.
A further benefit to Composite AI is that pre-built industry-standard knowledge graphs can be used. Users can then adapt those and develop their models for a specific industry. They need improving, but they are already applicable to most companies.
ML remains very powerful as a technology, but it can and has been improved. Adding knowledge graphs to ML and NLP – the Composite AI approach – is a much more effective way of extracting actionable insight from data and providing significantly more relevant and accurate search results.
Composite AI is the latest evolution of AI, improving the quality and scope of AI applications, and it is set to be the major AI trend for 2023 and beyond.
