However, many organizations are accelerating their efforts because of the ability of Composite AI to learn from incomplete or partial data sets, thus requiring less energy, which means lower costs; and it can handle “small data” workflows (high velocity) giving its users a competitive edge. This increase in activity is accompanied by capital inflows and the proliferation of businesses using Composite AI methods out of necessity. According to Gartner, composite AI currently makes up 5 to 20 percent of the way toward early majority adoption.
Companies can also use Composite AI for operational control across the entire value chain where the “smarts” of an enterprise reside. Composite AI enables us to build intelligent machines that combine human information with other forms of machine learning into one comprehensive system. The Intelligent Assistant acts as a virtual chief executive, applying Composite AI to the organization’s existing data to steer it in the right direction.
The goal of composite AI at this point is to link together “connectionist” types such as machine learning (or its subset, deep learning) with “symbolic” and other AI methods like agent-based modeling, rule-based reasoning, optimization, and graph analysis. The principles behind Composite AI are not new, but they have only recently started to materialize.
Furthermore, Composite AI understands that deep learning, graph analytics, or other “more traditional” AI approaches are not silver bullets.
Gartner found that, in addition to startups, “traditional” AI providers within established marketplaces like the data science and machine markets and knowledge graph vendors have started to integrate more AI capabilities, increasing the number of composite AI product groups available in the market.
Many business problems, especially in banking and insurance, require the capture of know-how, compliance rules, and other known knowledge from human experts in a structured manner and automatically reason with those real-world knowledge elements as a basis for new decision making.
Machine learning, rule-based approaches, and optimization methods can improve the ability to detect trends and patterns in the data. Thus, creating a composite AI system is the result of combining several technologies (among others) such as machine learning, rule-based systems, and optimization procedures.
The Mass is high because organizations need to make decisions faster or risk becoming irrelevant within their markets in a more complex business landscape. Time is money and the ability to automate repetitive aspects of back-office operations will free employees up to take on more value-adding tasks.
Traditional AI applications bring many benefits that Composite AI can also deliver across business functions, including:
- Customer service rep automation
- Reduced operational costs related to compliance/regulation updates
- Faster market research insights than competitors thanks to machine learning capabilities applied over fast data streams.
In addition, composite AI is required to deal with complicated logic or provide answers that are hard to express in natural language. In some cases, the best method to describe (and code) the solution necessitates using a variety of techniques to both capture business logic and learn from experience. Thus, composite AI is the next stage in AI’s growth.
Composite AI functions are helpful for any industry, sector, or company process. They do not take the place of current investments but rather enhance them quickly, hence justifying their usage. However, integrating AI technologies into existing application ecosystems is a difficult task. Despite that, combining AI methods lowers technical debt while simultaneously allowing developers to work at higher abstraction layers and benefiting from better segmentation of the knowledge gathered or required. This, in turn, opens the door to a larger range of stakeholders who can “code” their expertise more effectively, therefore, widening Composite AI’s appeal.
The days of relying on a single type of AI are drawing to a close. Software and service vendors that can’t provide solutions that incorporate various AI methods (such as machine learning, rule-based systems, optimization procedures, knowledge graphs, natural language technologies) will find themselves at a disadvantage compared to those who can. The introduction of composite AI methods into current products will have a significant impact on their capabilities.
One of the key takeaways from the report about Composite AI is to apply decision management with business rules, knowledge graphs, or physical models in conjunction with ML models to leverage domain knowledge and human expertise to give context and complementation to data-driven insights.