A framework for enterprise AI adoption
While there is a lot of excitement about how advances in artificial intelligence will help the enterprise sector, the reality is that most efforts fail. Study after study shows that organizations of different sizes are struggling to bring machine learning into their operations, and many initiatives end up being shelved or used in a very limited capacity.
The adoption of applied AI is very difficult and costly, wrought with pitfalls, and requires fundamental changes at different levels. However, as the tools and processes mature, more companies will be able to take advantage of enterprise AI while reducing the risks and costs of adoption.
Demystifying AI for the Enterprise, a book written by six experienced executives and thought leaders, brings to light some of the ways that organizations can ease their way toward applied AI. The book shares best practices and practical case studies of how AI is being applied to different industries.
Chapter 1 of the book, written by Prashant Natarajan, Vice President of Strategy and Products at H2O.ai, introduces IMPACT, a general framework that can guide the adoption of AI in the enterprise. Natarajan describes IMPACT as “a checklist to create business value and ensure the lasting success of digital transformation with data, analytics, and AI.”
Great products are built on top of great visions to address unsolved problems or provide solutions that are better than the incumbents. AI doesn’t change this. You’ll still need to have an eye for new markets, gaps in existing markets, and pain points in the lives of people and organizations. As Natarajan writes, “The most successful organizations—be they enterprises like Amazon or Tesla or Apple, or several start-ups and unicorns—thrive on creating new markets, new users, or even new uses for existing products.”
As other experienced people in the field suggest, a good AI strategy starts with knowing what problem you’re trying to solve. However, having a good understanding of the capabilities and limits of current AI technology will help you look at problems from a new perspective.
For example, the traditional e-commerce business model revolves around the “shop then ship” process. But with the power of predictive models, you can think about moving toward a “ship then shop” model. This means that items arrive at the customers’ homes before they even shop, and the machine learning model’s predictions are accurate enough to make the experience both convenient for the shopper and profitable for the seller. Another example is the healthcare industry, where service providers can think about improving the quality and reducing the costs of care and insurance through predictive care powered by machine learning.
“AI presents organizations with tried and tested methods to apply imagination by enabling leaders to leverage new data-driven insights and amplified/augmented intelligence… to go beyond current business value streams and processes, incorporate insights via new operational solutions and processes, or even refreshing existing ones to reach new users and markets,” Natarajan writes.
Thinking about the opportunities of enterprise AI is one thing. Having an organization that is ready to adopt AI technologies is another. Natarajan tracks an organization’s “maturity” to adopt AI across different dimensions, including strategy, leadership, processes, and data.
Writes Natrajan, “Evidence shows us that moving up the maturity curve is not only possible, but is necessary to transform your enterprise from an information-rich but insights-poor organization to one that is not only insights-rich but also an integrated cognitive enterprise where AI enhances and supplements human intelligence and experience to create an organization that can make use of the advantages of both human and machine intelligence to their utmost.
