AI pilot projects are everywhere. Small, successful tests succeed in controlled environments. However, few if any companies find success from them. The scalability of AI continues to be elusive.
C-suite executives often are not able to realize how difficult it will be to transition from pilot to company-wide usage. They often feel this will just be a technology issue, but it is more about the organization itself than the specific technology.
The Hidden Role of Data Fragmentation
Consider the example of a multinational bank using AI to monitor for fraud in their bank. In one region, the pilot project worked well. As they try and deploy globally, they discover that the data is inconsistent, and the regulations and cultures differ by country and region. What appeared to be a simple process suddenly has a very complex process.
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Alignment Is the Real Challenge
For AI to be successfully scaled, there needs to be alignment among all the organization’s resources. Technology, operations, legal and risk need to work together. When one group lags in their contributions, the arrangement will stall, and the challenge of scaling will be coordination among resources. AI relies on quality data, and companies that operate in multiple geographies often have disconnected and fragmented data infrastructures. While fixing these types of issues is less exciting than deploying AI applications, the impact on the success and speed of gaining value from these applications is significant.
From Experimentation to Enterprise Value
Leadership plays an important role in this transition. They must share their priorities to assure the implementation teams focus their activities appropriately. When these teams are not aligned, they pursue individual projects with minimal, if any, organizational impact, while increasing the collective amount of effort needed to do the sets of projects.
Additionally, senior management must provide appropriate incentives for the business to pursue AI. If the business units are measured on short-term results only, they will not pursue moving into the AI technology until they can see immediate results (and sometimes it takes time for the value from AI to be realized).
Many C-suite executives are trying to centralize AI deployments across their business units, while others are pursuing decentralized approaches. There are positives and negatives to both approaches and helping determine which is the best choice is contextual. Regardless of the chosen approach, clarity is going to be a crucial factor in the success of scaling the use of AI.
Employers also do not have confidence in AI, and developing this trust will take time. Trust is typically developed through consistent use of the AI technology, not necessarily through formal training.
The objective is not just about creating speed to value. It also has to consider the coherency of how these technologies, people and processes are evolving together and developing resulting business value on an on-going basis.
Without addressing the need for coherency, these companies will only be able to demonstrate the use of AI in their enterprise as an interesting experiment.
With coherency, AI can provide an ongoing source of business value creation over time.
