AI-BI-analytics

AI needs supporting datasets and streams of data to keep learning and improving. This impacts how BI works. Analytics are used to guide and drive the type of models AI uses. How analytics are done will change based on what the AI learns. There is constant communication and changes done back and forth between AI, BI, and analytics. Today, change management and risk management are mandatory to foster a healthy ecosystem.

The changes in the AI-BI-analytics ecosystem require changes in the way leaders manage and navigate through the ecosystem. When the ecosystem had a BI emphasis, cost management was the top priority for managing the ecosystem. When the emphasis moved to analytics, the top priority became opportunity management with the key activity of ranking the insights from analytics. Now, with companies emphasizing AI, the top priority for managing the ecosystem is risk management. AI makes mistakes and those mistakes expose the company to risks of heavy costs and revenue losses. The more companies delegate planning, decision-making, and manual work to AI, the bigger the impact AI mistakes will have on the company, its customers, and the market.

The demand for talent to support the AI-BI-analytics ecosystem is growing. Schools and training programs are focusing more on how to build using tech rather than on how to effectively apply that tech. Getting learners’ feet wet with the tech is taking precedence over how to best use that tech. That complemented with working remotely is resulting in lower-quality contributions to the ecosystem.

Knowledge sharing and transfer of tried-and-true approaches are slowed down and often hindered by not being in the same room. Web searches and web content are not substitutes for traditional knowledge transfer since algorithms prioritize what’s popular over what’s correct and right. The inappropriate balance between remote work and in-office work is a key risk to the health of the AI-BI-analytics ecosystem.