Adopting AI is a challenge-driven journey
Based on the insights from 2,525 white collar AI/analytics decision makers, this report shares the learnings from companies implementing AI and advanced analytics in business operations. Though the companies have reached different levels of AI maturity (divided between AI-leaders, AI-followers and AI-beginners) a commonality between the companies is that all have faced critical challenges along the way. But it is not the technology that is the main problem.
Key findings from the report
Ninety-nine percent of all respondents have faced AI/advanced analytics implementation challenges in their unit and ninety-one percent have had challenges across all three categories studied: technology, organization and people/culture.
The share of initiatives facing challenges goes up with increasing maturity – the more you learn, the harder it gets.
Eighty-seven percent faced more people/culture challenges than tech or organizational challenges. Similarly, 94 percent have deployed more people/culture solution strategies than other categories.
As many as 69 percent of AI-leaders foresee a constant flow of new AI/advanced analytics applications in their companies – new applications that, in turn, drive more process change and reorganization.
A full 63 percent of AI-leaders say that focus will shift from producing products and services to producing AI algorithms and models. At that post-transformational stage, the company may settle into a stable situation where the only constant is data-driven change.
What will it mean to be AI-driven?
Post-transformation, companies will settle into a stable situation of constant data-driven change. In order to remain competitive, employees will need to gradually let go of category expertise, as core focus will move away from the production of products and services, and modeling of data will drive the development of offerings and strategies.
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