Two essential factors are required to ensure the success of corporate AI research.
First, the structure of successful AI R&D organizations is rather peculiar: different successful approaches emerged, including Google DeepMind acquisition, Facebook AI Research (FAIR) bootstrap from University teams, and OpenAI not for profit mdoel.
A key similarity to all these operations is the focus on aggressive long-term missions and the fostering of stable research teams and open collaborations. To achieve success in the competitive environment that is AI today, it's imperative to ensure stable operating structures over medium-to-long term.
Next, to assure the transfer of innovation to production, successful organizations foster vigorous interactions with operating business units, while at the same time avoiding short-term budgetary pressures and direct reporting structures. Rapid engineer to scientist level of interaction are the results of co-locating R&D and product groups in the same building, and not the result of slow and cumbersome reporting structures.
The second factor relates to how research is conducted: world-class AI projects are carried out in cooperation by numerous researcher institutions, at both companies and universities, making open and early publication of code and results a fundamental factor. In order to protect ideas, early publication of research findings and source code on arXiv, GitHub, is a very cost effective alternative to patents. Leaving precious research locked up in drawers is not in the interest of any company, because the technology will not be adopted and the development will stall. Active publication of information and source-code is the main reason behind today rapid progress of AI. No serious player of the AI sector can expect success from operations without actively contributing to the open-source ecosystem.