The growth of the artificial intelligence industry is undeniable. In 2021, Gartner predicted that the global market would top $62 billion by the end of 2022.
AI has endless business applications—proven by the fact that nearly 40% of organizations are already using it in some form or another. But, day by day, business problems are only getting more complex, requiring innovative new techniques that can rise to the challenge.
Enter: Composite AI.
In this post, we’ll break down what composite AI is, how it works, and what benefits it provides to businesses that leverage it.
What Is Composite AI?
Composite AI refers to the combination of various AI technologies. Traditional AI is a broad sector of machine intelligence and has a wide range of applications, including automation, predictive analytics, machine learning, natural language processing (NLP), and so on.
In many cases, businesses need to decide which AI applications best fit their use case and how to integrate multiple types of AI for the best outcome. This is where composite AI comes into play.
Gartner’s 2022 Hype Cycle for Artificial Intelligence puts composite AI in the “innovation trigger” category and has listed it as one of seven techniques expected to see “transformational” benefit in the next 2-5 years. The report cites composite AI’s ability to combine different AI techniques to achieve better results and states that it brings the power of AI to more organizations and helps expand AI applications’ scope.
Composite AI is on Gartner’s radar, and it should be on yours, too. By embracing this technique, enterprises can build strategies that support the hyper-growth and innovation driving the modern business landscape.
How Does Composite AI Work?
Composite AI is not one unique combination of AI techniques, but rather an approach to understanding how an organization can benefit from layering various AI technologies. With the varied applications of AI available, finding a solution for a business is rarely choosing a single strategy. Instead, the best business solutions often require a combination of relevant AI strategies.
Let’s take, for example, a retail organization seeking to maintain competitive pricing and respond to consumer trends and demands in real-time. Their solution may be to use a combination of predictive models and forecasting techniques to anticipate consumer demand and trends, optimize future pricing, and maintain an edge over competitors.
Clearly, composite AI allows businesses to level up how they’re using their data. So, how can you make a plan to use composite AI at your organization?
- Define the problem. What are you hoping to solve?
- Select the AI techniques. Do I have the data, expertise, and tools needed to use my desired techniques?
- Create models for testing. Have my data science teams successfully tested and adjusted these models before they go live?
- Gradually implement and integrate each AI technique. Do I have a plan to implement these process changes gradually and address any problems as I go?
The Benefits of Composite AI for Business
Enterprises can use composite AI in countless ways—combining AI techniques is much more effective than relying on just one. A few key benefits include:
- Fewer data prerequisites: Composite AI can work even if your organization has limited data availability or smaller datasets—users have seen success coupling what data they do have with synthetic data from GANs or knowledge graphs.
- Determine the best action: By combining ML- and analytics-based AI, you can conduct prescriptive analytics and ensure the insights you garner will guide you to the best decision possible.
- Leverage emerging tech: Computer vision, NLP, deep learning, and agent-based modeling all have increased capabilities with composite AI. Determine the speed of objects within images, model complex decision-making processes, and manage your entire supply chain.
Besides the general advantages that can be reaped across industries, there are specific advantages for each sector as well. To illustrate this, we’ve laid out examples of composite AI’s impact in financial institutions and manufacturing.
By leveraging composite AI, financial services institutions will find they can work with sensitive data faster and more accurately. A few more benefits include:
- Detecting fraud. Analyzing massive amounts of data to detect fraud can overwhelm both personnel and legacy systems. Composite AI solutions like NLP and data mining automate behavioral analysis and send customers a note when they suspect fraudulent activity.
- Automating anti-money laundering (AML) investigations. AML investigations are labor-intensive and time-consuming. Banks are now using knowledge graphs and machine learning models to examine transactions at scale, identify suspicious activity, and increase the rate of true positives.
- Providing better customer service. By providing customers with personalized service via ML algorithms and knowledge graphs, financial institutions are increasing customer satisfaction and optimizing conflict resolution.
- Speeding up compliance checks. A financial institution might leverage NLP to gather critical data from documents, along with AI-powered automation to speed up related internal processes.
Manufacturing is another key area where composite AI is making strides. Here are a few key examples of its potential use cases:
- Informing predictive maintenance. Heuristics (strategies derived from previous experience) about certain sensor readings, combined with deep learning, can predict an asset’s health and if it’s fit to operate.
- Reducing waste. AI-powered systems can be infinitely more efficient than legacy systems, reducing material waste. Automated maintenance results in less downtime and longer lifecycles for plant machinery.
- Managing the supply chain. By using demand forecasting models to estimate future demand alongside digital twin technology to simulate how changes would have an impact on your business, you can start optimizing your supply chain operations.
Composite AI opens up a ton of possibilities for your organization to leverage advanced AI techniques. With the power to combine ML, image recognition, deep learning, NLP, and more with (essentially) no dataset size limits, the world is your oyster.
But, there’s still one thing missing. How do you best leverage your team’s human expertise to complement data-driven insights?
We’ve got a few pointers. Check out our blog post, How to Bring Data Science Closer to Your Business, to get some tips on upskilling your workforce so that composite AI (and data science in general!) can effectively spread throughout your organization.