Peter K. Jackson, a counsel in Greenberg Glusker’s intellectual property group, tells a hypothetical story about creating (or buying?) a responsible, useful and risk-aware AI tool.
C-suites everywhere are awakening to the tantalizing efficiencies AI solutions can unlock. Most leaders realize that outpacing competitors means going beyond chatbots and other off-the-shelf tools. Many understand that achieving true gains with AI requires processing their business’ proprietary data in novel and potentially risky ways.
Outside the machine-learning space and enterprise companies, uncertainty and fear of risk often stymie efforts to advance AI ideas. Even approaching the procurement conversation can seem perilous. To build useful and trustworthy AI solutions, many businesses will have to confront cross-department questions and choices they’ve never faced before.
Whether you’re worried about internal roadblocks or ready to build (or buy) your own AI solutions, here’s how to start the conversation, choose the right team, sketch out your compliance roadmap and steer away from future legal hazards.
These steps should apply to most custom AI journeys, which are often abstract. So, to illustrate a hypothetical journey, we’ll follow a global consulting firm as it chooses among desirable AI use-cases,…
