1. The monetization mindset
2. Data is more than the new oil
Data is more than the new oil. When we look at data as oil, we see it as an existing asset type. A barrel of oil is gone after you use it once. Data is different because it can be monetized repeatedly without being used up.
3. Asking questions
When businesses start with questions like, “What can data do for us? What can AI do for the business?” monetization rarely materializes. Those are the most expensive questions the business can ask. That thought process often leads to data and AI initiatives failing to deliver value for customers or the business. There’s something that must always come before the technology.
4. What first?
AI first is the wrong way to think about monetizing, and that’s probably the last thing you expected me to say. We must switch away from data first, AI first, or any technology-first approach. Why?
The business’s purpose is not to create technology. Very few companies exist to deliver technology alone. The purpose of the business is to create value for customers. How do we deliver value to customers? Products, so instead of looking at this as data first, AI first, or any technology first, let's start looking at this as a value-centric problem that puts the product first.
5. Thinking product first
When that happens, we ask 2 new questions. First, what do the business and its customers need? Second, what opportunities do business leaders see today? Technology fits at the end of both questions. What parts of both cannot be seized with current business and operating models? What opportunities require something new, in this case, data and AI, for the business to seize an opportunity or to meet a need?
6. Two sides
Now we're beginning with the need and asking the questions, ‘What can’t we do today with digital technology, with cloud, with all of the other available technologies? What new value can we create? What new ways of creating value do data and AI enable that are aligned with opportunities business leaders see? What value creation aligns with business needs and customer needs?’
That's what a product-first approach changes. We must evaluate two sides of the business: the value and the workflow. Workflows cover familiar ground, the execution, implementation, products customers use, and how customers use them.
7. Value Stream
The value stream connects two sides. Value streams connect strategy with execution. Data and AI can improve existing value streams by optimizing workflows. Data and AI enable new value streams that can only be supported by the technology, data and AI. These workflows were never possible before. By leveraging data and AI, businesses create value in new ways and deliver new value types to customers.
8. Product first is product-focused
A product-first approach is product-focused. Products must meet specific criteria, which is one of the key benefits of adopting a product-first approach. They must function in production environments, the real world, for them to create value. They operate in an environment where they can deliver value by supporting workflows. When you look at it from a workflow perspective, products must function and fit into that workflow.
Data and AI products must meet user and customer requirements. Once products integrate into the workflow, they must be built for customers or internal users to adopt.
Data and AI products must be reliable, not just functional. Data and AI products must work with users and customers, not fight them.
Finally, the business must be able to monetize the data and AI product to generate measurable returns. Those can be cost savings, productivity improvements, or charging customers to generate more revenue. It must be monetized in some way, and more importantly, this business, not some hypothetical business, must be able to monetize it to generate significant returns.
9. Let's practice!