Business leaders are frequently advised to “walk before you run” when integrating enterprise AI (EAI). I agree it makes sense to start with low-hanging fruit, but overall successfully integrating AI into an enterprise depends on development and execution of a comprehensive, long-term strategic plan before even the simplest machine learning projects are undertaken.
There are four key components to an enterprise AI strategic plan that must be executed in lockstep: machine learning, data and platform, corporate culture, and workforce management.
It is important to link EAI projects to clear and measurable business results, rather than picking a popular application and hitting go. When there is a clear understanding of how AI can be applied throughout an organization, leaders can assemble an intelligent project roadmap that prioritizes and maximizes near-term ROI. Early feasibility assessment of each potential project can identify data gaps and data quality issues that can be remedied in parallel with early projects, potentially accelerating future projects by months or even years.
Data is the fuel of enterprise AI. Machine learning models require that data be co-located in a new type of file storage system. The majority of data will ultimately be shared across functional groups within the organization. To ensure that the data required for each project is available when needed, it is imperative to create an end-state vision for the data systems architecture, and a staged plan synchronized with the machine learning project roadmap to achieve it.
EAI is fundamentally a human endeavor, and people’s needs must be addressed simultaneously with business and technology requirements. In a dynamic environment, an organization that embraces a learning culture will thrive. Employees are united by their curiosity, leaders emphasize knowledge and creativity at all levels, and the organization maximizes the true power of data-driven decision making as a team.
Practically everyone in an organization will be impacted by EAI adoption, so a proactive workforce management program is essential. The program must actively manage employee fears, acquire and grow data science talent from within, address retraining for displaced workers, and provide the new skills that everyone in the organization requires to work alongside AI. A good place to start is by clarifying the underlying infrastructure and roles that make data science teams effective.
A plan that addresses all four of these components in tandem presents a comprehensive view of the future, with strategic and intelligent prioritization of machine learning projects. It provides an end-state vision that can be evangelized, creates inclusion, and strengthens an organization’s commitment to staying the course through transformational growth.
To go a bit deeper on each of these four components check out our process. The team at milk+honey built this approach to address both the technological and human factors involved in this unprecedented growth opportunity, and made sure it sets business leaders up to overcome the obstacles that most often impede successful enterprise AI integration. Our end goal is to help you develop machine learning as fundamental competency inside your existing structures.
I'd love to hear from you too: How are you implementing enterprise AI? As enterprise AI moves beyond the hype cycle and into action, I believe it's more important than ever that we share among our community to unlock its promise. Please feel welcome to comment, connect, or contact us at milk+honey.
Finally, as part of milk+honey's mission to bridge the gap between data science and business, I'm writing weekly about enterprise AI integration. Feel free to send any questions or topics of interest our way and I'll include them in the line up!
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