One of the biggest mistakes in AI strategy is copying another company’s playbook. Leaders read about a big win at a tech firm and try to do the same thing. However, the context is always different. As a result, the copy often fails. Understanding the three paths to the AI enterprise helps leaders avoid this trap. In other words, there is more than one right way to build AI into a business.
JPMorgan Chase, Ping An, and Siemens show three very different AI journeys. They come from three industries and three continents. Yet all three reached the same insight. Specifically, AI value depends not just on the tools but on how a company organizes around them. Together, these cases map the three paths to the AI enterprise better than any theory could.
For leaders, the key question is not “who does AI best?” Instead, it is “which path fits our situation?” Moreover, copying a model that does not match your culture or data will waste time and money. Therefore, leaders must first understand their own strengths before choosing a path. For a deeper look at building AI capability, see our guide to enterprise AI transformation.
Path 1 to the AI Enterprise: Competitive Infrastructure (JPMorgan)
JPMorgan shows the first path to the AI enterprise. It means adding AI deep into daily operations without changing the whole business model. By 2019, the bank had about 50,000 tech workers and spent over $11 billion a year on technology. For example, its COIN platform could review loan deals in seconds. Previously, that work took an estimated 360,000 hours of lawyer and officer time each year.
What makes JPMorgan’s path special is its step-by-step approach. Rather than betting on one big AI project, the bank built hundreds of models across many areas. These include credit risk, fraud detection, trading, and compliance. As a result, no single failure could derail the whole effort. Moreover, each new model added more value to the shared data platform. In other words, the returns grew over time.
This path suits companies with large data sets, strong processes, and a low appetite for risk. However, it also has limits. Specifically, it tends to improve what already exists rather than create something new. Therefore, leaders who choose this route must accept that AI will boost current operations, not reinvent the business. For more on this kind of strategic choice, see our post on AI strategy.
Path 2: AI as Business Reinvention (Ping An)
Ping An took a bolder route. It represents the second and most disruptive path to the AI enterprise. China’s largest insurer rebuilt its whole structure around a shared tech backbone called Ping An Technology. From there, the company moved far beyond insurance. It expanded into healthcare, banking, smart cities, and auto services. For instance, its AI-powered Good Doctor platform grew to over 400 million users. That scale would not be possible without the shared tech platform underneath.
This path demands the highest risk tolerance of the three. It requires leaders to rethink revenue streams, change reporting lines, and invest in shared platforms before seeing returns. Consequently, it also offers the highest upside. Specifically, it can create new business models, new markets, and network effects. However, few companies have the culture or patience to follow this route. In fact, most will find it too disruptive for their current structure.
Leaders drawn to this path should ask a tough question. Can we truly accept short-term losses for long-term gain? Moreover, do we have the data and talent to build a shared AI platform? If the answer to both is yes, this path can yield results that competitors cannot easily copy. Nevertheless, the risks are real and the timeline is long.
Path 3: AI as Industrial Transformation (Siemens)
Siemens shows that physical industries face a very different AI challenge. As a result, they need a different strategic path. While JPMorgan worked with financial data and Ping An built consumer platforms, Siemens had to put intelligence into factories, power plants, and trains. In other words, the AI had to work in the physical world, not just on screens.
Siemens built its strategy on digital twins, predictive maintenance, and automation platforms. It also partnered with NVIDIA and Microsoft to fill gaps. Moreover, platforms like Xcelerator gave industrial customers easy access to AI tools. However, the hardest part was not the technology. Specifically, it was earning trust from engineers used to physical certainty. These workers now had to act on AI predictions. Therefore, change management was just as important as the tech itself.
This path suits companies that make or manage physical things. It takes longer than the other two paths because the real world moves slower than software. Nevertheless, the moat it builds is very strong. In fact, competitors cannot easily replicate years of sensor data and domain expertise. For related insights, explore our analysis of AI leadership skills needed for this kind of transformation.
Choosing Your Path to the AI Enterprise
The three paths to the AI enterprise are not one-size-fits-all. Each one fits a different kind of company. Specifically, the right choice depends on your industry, your data, and your risk appetite. Therefore, leaders should study all three before picking one. Moreover, some companies may blend elements from more than one path as they grow.
What all three companies share is a key insight. Specifically, AI success is not about having the best algorithms. Instead, it is about building the right structure around AI. In other words, how you organize matters more than what tools you use. Consequently, the first step is not to buy software. It is to align your leadership, culture, and data around a clear AI vision.
In conclusion, the path you choose will shape your company for years to come. Therefore, take the time to understand your own strengths and limits first. For more on how to lead this kind of change, read our guide to the AI leadership diamond. Furthermore, remember that no path is risk-free. However, doing nothing carries the biggest risk of all.
