In today’s advanced age of technology enablement, have you ever wondered why a large number of organizations are struggling to adopt artificial intelligence/machine learning (ML) successfully? Countless organizations are increasing interest and pumping investment into incorporating ML, and many of them have successfully identified valid use cases to kick-start their ML journey. Yet, business leaders still wrestle with identifying meaningful results that stem from their AI/ML investments, and much of that starts well before the deployment of algorithms.

Activities such as building out the technology environment and prepping the data create seemingly trivial, but increasingly significant challenges that influence whether AI/ML programs succeed. The initial excitement goes away, and these programs often get shelved. In most cases, these initial challenges can be avoided by instituting more rigorous thinking through each implementation step and by being better prepared to address them as they arise.

If you have ever had this “failure to launch” experience before or are “sitting on the fence” right now, the following suggestions will help you overcome many of the obstacles that impede your ML journey.

READINESS OF THE LARGER ORGANIZATION

Initially, business leaders perceive ML as a disruptive technology. Most likely, its introduction will force business process, functional, organizational and even behavioral changes, depending on the nature and scale of the project. For example, in a predictive maintenance ML effort, managers must take into account the ripple effect that an implementation will have on areas of the business ranging from the maintenance process itself to labor relations. To enhance the probability of business success, leaders must evaluate how their solution affects these interdependent elements of the enterprise before commencing with implementation.

The first step is to conduct an impact assessment to determine the potential disruption the new solution may cause. For example, a solution that requires a large number of complex process changes is almost certain to have difficulty getting through the internal approval process. Of the many issues that AI/ML project leaders will face, organizational changes or behavioral aspects of the change will be the most challenging. Enlisting a senior-level business sponsor will help to sell the project internally and to navigate through the challenges of addressing the organizational roadblocks and required process changes.

IS YOUR DATA READY?

Deploying ML means creating complex ML data models which analyze large data sets within a shared environment. Therefore, transactional data elements and master or reference data (e.g. customer and product details, location, etc.) must be captured, cleansed, enriched, correlated, and stored in a location that is easily accessible for your data scientists. Note that data scientists typically want to analyze raw data, so make sure that the historical, raw data structure is conveniently accessible. With various business departments serving as both the producers and consumers of data, the IT organization plays an important partnership role as the enabler by producing enterprise-wide data stores (e.g., data lake, enterprise data warehouse, or departmental data marts).

Furthermore, it is crucial to store data in a modern data architecture, where business consumers have the necessary knowledge and access privileges to consume it. Deploying a modern Data Architecture along with the adoption of a mature Data Management process, will ensure that enterprise-wide data and its ML-produced insights are available to business consumers.