For the last couple of years, many companies of virtually all sectors of the business market ? including finance, government, retail, telecommunications, utilities, energy, and transportation ? have been enthusiastically considering the evaluation and management of AI-based projects. Some of them even consider this technology as the cornerstone of their digital transformation journey. However, as per a survey report by Peeriosity, only 13% of organizations are currently using AI. The low rate of AI implementation calls for speculation so as to evaluate why the AI adoption in AR is low, despite the sensation it has created. This section evaluates the challenges in AI adoption. The root cause of resistance is information asymmetry. While there is a lot of chatter and hype around AI adoption in AR, businesses struggle with information asymmetry when looking out for options. The overuse of buzzwords has created a grey area in the minds of the A/R professionals regarding this technology and its applications. Credit and AR managers struggle to cut through the smoke and mirrors of buzzword-happy software vendors to understand what AI means for their processes. Moreover, the vendor landscape is highly segmented with different types of vendors offering different solutions and diverse portfolio ? process automation vendors, custom AI shops and RPA vendors. Consequently, there is no clear understanding of what the expectations should be from an AI project leading to poor estimation of project ROI and benefits. Confused ROI expectations result in difficulty in stakeholder alignment and gaining buy-in from decision makers. The result is failed attempts at bringing in artificial intelligence to the A/R departments, therefore, the low adoption. The first step to resolve the AI tangles is to understand the vast landscape of AI and automated solutions available today. The next section takes a deeper look into the fragmented technology vendor landscape and why is it difficult to choose an AI technology partner.