What Is AI-Driven Growth?
In a previous post, I called data and AI new inputs for growth. During my Data and AI Product Management Certification, I define what it takes to turn inputs into business impacts like productivity, cost savings, and revenue. This Saturday, I get to start the process with a new cohort. There are a few spots left, and I would enjoy the opportunity to work with you over the next 6 weeks.
Why spend so much time on definitions and frameworks? I got into this during office hours today, and it was a great question. Without definitions and frameworks, this is all fluffy thinking that can be bent in any direction. Innovation, monetization, growth, and all these other terms are difficult to define, but we must do the difficult work, or they won’t be successful.
In this article, I’m doing that with AI-driven growth and explaining why the growth trajectory is different.
Digital VS AI-Driven Growth Curves
Data and AI deliver a growth curve that’s different from any before it, requiring new frameworks. Product management must adapt in response because these growth curves only materialize if we stop treating data and AI products like digital products.
This image shows the difference between digital and AI growth paradigms. Given it’s much easier to reach the 100M user mark now than when Instagram and Pinterest launched. However, the difference in AI-driven growth is helped by another factor.
This image shows a stark difference between GitHub Copilot and Power Platform Copilot. Why is the navy-blue line showing different characteristics from the orange? GitHub Copilot has more of a digital footprint in that it’s deployed for a very narrow use case. Power Platform Copilot is deployed like an AI product and has a much broader, less constrained utility.
Digital products can benefit from data and AI features integrated into the existing product. They can also be augmented by AI products, like Copilot, which augments GitHub and Power Platform. In both cases, the implementation determines which curve growth will follow.
If AI products are designed to fit a narrow use case or set of use cases, they will follow the digital growth paradigm. If they are designed more openly, users will find more use cases. AI supports user intent differently than digital.
Digital requires code to contain the user or customer’s intent, which is why it only supports a narrow range of intents. AI can detect intent and serve a range of intents as broad as its training data. We must design AI products to support the use cases we built them for and openly enough so that people can discover or build their own use cases.
The AI-Driven Growth Curve Is A Maturity Model
Where does your business fall on this scale? Companies that are 2+ steps ahead dominate their markets, and competitors are unlikely to ever catch up. This is the Great Business Die-Off that’s happening now. Companies are built to respond to or catch up to linear growth, but AI-driven growth is different.
Zombie: Current capabilities are so far behind, or internal transformation barriers slow maturity progression so much that the company will never catch up. Adoption accelerates with AI products, and market share is acquired rapidly. There is less time to respond, so capabilities must either be near the competition’s or the business must be able to transform quickly. Zombie companies have neither so an AI product will put them out of business.
Legacy: Technology is implemented, and data is gathered unintentionally, disconnected from value creation. Even though the business has data capabilities, it can only deliver digital products. The business must begin transformation this year or fall into the Zombie category.
These companies keep me up at night because most industry surveys put 40% to 50% of all businesses in this category. I have been warning about the Great Business Die-Off for a few years because so many businesses are at risk of failing over the next 2 to 3 years.
Competitive Maintenance: The business invests enough to maintain its market position. Those investments don’t meaningfully impact revenue and margins, so technology is a cost center. These businesses are in a much safer position, but there’s a significant risk.
Businesses that don’t see technology as a growth driver miss their biggest opportunities. AI-driven growth can’t materialize if business leaders don’t think technology is an input for growth. Any investment that doesn’t create growth will, eventually, be cut back.
Competitive Advantage: Data and AI are value drivers, accounting for 25% to 50% of annual revenue growth and savings. A mature opportunity discovery process surfaces and defines high-value initiatives. The opportunity pipeline is critical because it’s the justification for continued investment that takes technology teams out of the cost center category.
Many of the frameworks I teach in my AI Product Management Certification support creating the opportunity pipeline and turning opportunities into products.
Innovator: Data and AI are the primary drivers of growth and cost savings, accounting for over half of both categories. A mature innovation pipeline delivers large, long-term growth opportunities. Innovation doesn’t mean much if products aren’t delivered and monetized.
AI Product Managers connect the research lifecycle to value creation. That’s how the uncertain process delivers artifacts that become products and deliver growth.
Accelerator: The business delivers products and platforms that accelerate its customers’ data and AI maturity progression and value creation. AI product revenue will deliver double-digit growth for the next 2-5 years. When AI becomes the product, platforms enable the novel monetization paradigm.
Platforms also enable ecosystem business models that monetize customers, partners, and developers. AI products designed to serve multiple intents and an ecosystem start the climb up the hockey stick.
Foundation: The business provides platforms and ecosystems on which AI products are built. Revenue is growing as much as 2X-3X annually. NVIDIA is the first AI example, and AWS did this for the cloud. The rest of the AI foundation platform landscape is still up for grabs. Startups like OpenAI or companies like Google should have had the inside track, but technology doesn’t matter if the business can’t turn it into a cohesive platform.
Until a business has systems, models, and frameworks that explain complex and uncertain AI product strategy concepts, there’s no way to realize an AI-driven growth trajectory.