Ready or Not, AI Comes— An Interview Study of Organizational AI Readiness Factors

Artificial intelligence (AI) offers organizations much potential. Considering the manifold application areas, AI’s inherent complexity, and new organizational necessities, companies encounter pitfalls when adopting AI. An informed decision regarding an organization’s readiness increases the probability of successful AI adop- tion and is important to successfully leverage AI’s business value. Thus, companies need to assess whether their assets, capabilities, and commitment are ready for the individual AI adoption purpose. Research on AI readiness and AI adoption is still in its infancy. Consequently, researchers and practitioners lack guidance on the adoption of AI. The paper presents five categories of AI readiness factors and their illustrative actionable indicators. The AI readiness factors are deduced from an in-depth interview study with 25 AI experts and triangulated with both scientific and practitioner literature. Thus, the paper provides a sound set of organizational AI readiness factors, derives corresponding indicators for AI readiness assessments, and discusses the general implications for AI adoption. This is a first step toward conceptualizing relevant organizational AI readiness factors and guiding purposeful decisions in the entire AI adoption process for both research and practice

Sea level and dynamic topography in the Western Pacific during 1982-83 El Nino

Pendant le fort El Nino 1982-1983, deux réseaux d'observations océanographiques ont fourni des données continues dans le Pacifique tropical : le réseau de mesure du niveau de la mer créé et maintenu par l'Université d'Hawaii et le réseau XTB - navires marchands du groupe SURTROPAC, Centre ORSTOM de Nouméa - La topographie dynamique mensuelle 0-400 db est calculée le long de la route Nouméa-Japon (20°S-20°N, 150°E-165°E), en utilisant des relations T-S réelles, pour faire ressortir les anomalies interannuelles. Cette topographie est comparée aux valeurs mensuelles du niveau de la mer. Ce très bon accord obtenu entre les variations spatio-temporelles déduites des deux réseaux montre qu'ils se complètent utilement dans la surveillance des anomalies climatiques. (Résumé d'auteur

Modes of ocean variability in the tropical Pacific as derived from Geosat altimetry

Satellite-derived (Geosat altimetry) sea surface height anomalies for the period November 1986 to September 1989 were investigated in order to extract the dominant modes of climate variability in the tropical Pacific. We applied the technique of principal oscillation patterns and computed associated wind stress patterns for each mode. Four modes were identified. The first mode has a time scale of about 3 months and can be identified with the first baroclinic equatorial Kelvin wave mode, which is excited by intraseasonal wind variations over the western equatorial Pacific. The second mode has a time scale of about 6 months and describes the semiannual cycle in the tropical Pacific sea level. Equatorial wave dynamics appears to be crucial for this mode also. The third mode is the annual cycle which shows evidence of off-equatorial Rossby wave propagation. The fourth mode is associated with the El Niño/Southern Oscillation (ENSO) phenomenon. The ENSO mode is found to be consistent with the “delayed action oscillator” scenario

Surface currents of the Eastern Tropical Pacific Ocean

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An Investigation of Short-Range Climate Predictability in the Tropical Pacific

The predictability of the El Niño/Southern Oscillation (ENSO) phenomenon was investigated by analyzing observed sea levels, surface stresses, and subsurface temperatures simulated with an oceanic general circulation model forced by observed winds. In addition, a large ensemble of prediction experiments has been conducted with a simplified coupled ocean-atmosphere model consisting of an oceanic general circulation model coupled to a simple atmospheric feedback model. Our analysis supports the hypothesis that the ENSO-related interannual variability in the tropical Pacific can be understood as a cycle within the coupled ocean-atmosphere system which is inherently predictable. This cycle consists of an accumulation of warm water in the western Pacific during the cold phases of ENSO and a loss of this heat during its warm phases. The results of the prediction experiments with our simplified coupled ocean-atmosphere model indicate that the phase of tropical Pacific sea surface temperatures is predictable two to three seasons in advance with our simplified coupled system, whose dynamics is governed by the ocean. We found a strong dependence of the skills on season, with spring SSTs being least predictable