Innovations generated and supported by artificial intelligence (AI) are now within the reach of most companies. However, it’s a tremendous challenge to make AI and related technologies—the Internet of Things (IoT) and machine learning—work together securely at commercial or industrial scale to create business value.   Even more challenging is to put smart technologies at the point of use/consumption and integrate that intelligently into the valuestream. 

Until those technologies are connected to form a new value stream, it will be very difficult for any company to generate returns that justify their technology investments.

Think beyond use cases. Consider a global paint manufacturer working to become a business with data at the core of its operations. It can innovate value-added services that generate recurring revenues instead of one-time product sales, by developing a “smart paint” that takes advantage of "smart dust" infused in the paint.  An example would be container ships, where the paint will transmit data not only about the condition of a ship’s hull and the paint itself but also about the water it moves through, including its temperature and oxygen content, and the presence of pollutants. An AI platform would determine not only optimal time to apply anti-fouling treatments to the hull, reducing drag and increasing energy efficiency, but by integrating multiple use cases, could be the foundation to transform the operation into a digital business. This means using data to improve its own operations, along with forging ties with other organizations that can derive value from the information.

By thinking beyond use cases that work in isolation, companies have the potential to upend an industry, create a new product category, shape a new business model or reconfigure a value stream from end-to-end, increasing top-line value and bottom- line contribution. 

Integrate a multipurpose AI suite. Such integration is possible only with a multipurpose AI platform. Today’s AI scene is dominated by single-purpose AI applications such as search engines and product-recommendation engines. Evolving rapidly, however, are AI suites,  that have capabilities that can be applied to a range of business needs, waiting only to be configured and loaded with data relevant to the problem at hand. Using such platforms, an organization can apply AI across a range of activities and create new value streams that can dramatically improve business results. 

Take advantage of an experienced guide. Most companies will need to undertake their AI transformation with the help of a guide and collaborator that can provide, tune and implement the AI platform and help create capabilities as the company builds out its ecosystem. 

Ideally that guide can bring together the formidable technical capabilities needed to create the ecosystem, the deep understanding of workflows and business processes that can transform how work is done, and the domain expertise that enables the organization to identify the highest-value use cases and generate the greatest value from its AI, machine learning and IoT assets. 

I've recently co-authored a point of view, "Using Artificial Intelligence to Create New Value Streams," and invite you to join the discussion to think big about AI.  It might sound risky, but the greater risk is to think small and be left irretrievably behind.