As enterprises move beyond the pilot stage to scaling and operationalizing artificial intelligence, one tech giant is changing the way its AI operations are organized within the company. Meta (Facebook's parent) announced in early June that it would decentralize AI at the company, distributing ownership of it into Meta's product groups, according to CTO Andrew Bosworth. "We believe that this will accelerate the adoption of important new technology across the company while allowing us to push the envelope," Bosworth wrote in his post announcing the change. The announcement signals a shakeup of how AI is organized at Meta, with the VP of AI Jerome Pesenti leaving the company and other changes such as the consolidation of several separate AI teams. The changes at Meta beg the question for other forward-thinking enterprises across industries: 'Is Meta's AI reorg the example to follow? How should we think about structuring our own artificial intelligence research and operations?' Often, enterprise organizations get their start with AI as an initiative driven by a single business unit.

Decision intelligence is one of those terms that sound vaguely familiar, even if you've never come across it before. Like many category-defining terms, it can mean different things to different people. This is a feature category-defining terms either have by design, or acquire through extensive use. Gartner defines decision intelligence as "a practical domain framing a wide range of decision-making techniques bringing multiple traditional and advanced disciplines together to design, model, align, execute, monitor and tune decision models and processes. Those disciplines include decision management (including advanced nondeterministic techniques such as agent-based systems) and decision support as well as techniques such as descriptive, diagnostics and predictive analytics". Erick Brethenoux, a distinguished VP analyst on artificial intelligence (AI) data science and decision intelligence (DI) at Gartner, frames DI as, "a practical discipline used to improve decision-making by explicitly understanding and engineering how decisions are made, outcomes evaluated, managed and improved by feedback".

IT organizations across the board are scrambling with new and different priorities in response to changes brought by the COVID-19 coronavirus pandemic. Supply chains have been disrupted, customer service needs may have changed, and employees may be working from home. But the biggest changes are probably at IT organizations within government and healthcare organizations in particular. These organizations are tackling the pandemic head-on, applying technology to solve issues around supply chains for PPE and ventilators, tracking the spread of the disease, performing contact tracing on confirmed cases of the virus, and more. There's a lot to do, and there's an urgency to do it all last month.

Companies should be adjusting their data and analytics strategies to better align with market realities as they unfold. Up until a few weeks ago, it was relatively clear that companies needed to become increasingly digital to thrive in an era of rampant industry disruption. Regardless of whether businesses have been shut down or they're operating above or below their normal capacity, every company's data and analytics strategy has been impacted because the underlying data has changed. Customer behavior has changed, supply chain behavior has changed, company operations have changed. If your data and analytics strategy isn't keeping up with what's happening, then you have important work to do, quickly.

And one such unknown today is artificial intelligence. Is it right to be afraid of AI? Or is this just an irrational fear of the unknown? To make artificial intelligence more understandable to its workforce, the Air Force Research Laboratory Materials and Manufacturing Directorate recently invited Dr. Erick Brethenoux to explain how it all works and how we all can expect to benefit from it in the future. Brethenoux specializes in machine learning, artificial intelligence and applied cognitive computing on the AI team at Gartner Inc., a consulting firm AFRL information technology uses for help with its mission-critical priorities. To begin his talk, Brethenoux reassured his audience that artificial intelligence doesn't really exist.

Your organization may look like it is well on the way to a machine learning future. Your team is beyond the basics. They are now creating machine learning models that could impact real business problems. Yet they can only do that if those models are implemented. Once those models are created, many organizations seem to be experiencing a disconnect in getting them implemented.