Operationalizing AI — A Report
This report we’ve written with O’Reilly Media, “Operationalizing AI: How to Accelerate and Scale Across People, Processes, and Platforms” contains our thoughts on how to help organizations understand what does and does not work in in practice when it comes to companies building predictive solutions. If you’re in a data science team tasked with building predictive services, this a brief report for you with essential principles on how to organize your teams and tooling you to accelerate and scale your work.
As the subtitle says, it’s a review of “How to Accelerate and Scale Across People, Processes, and Platforms.” It includes case studies from industry leaders like and Red Bull and IBM clients like Wunderman Thompson data. There are common themes shared across their stories and we try to extrapolate and generalize them for you.
The full report will be available for download later this month through the IBM Data Science Community, but here is an excerpt for a start:
“Increasingly, across industry sectors data scientists are consulted on a daily basis about making sustained and fundamental changes to the way their organizations do business. However, their organizations are typically not designed to realize the benefits of those changes. Company leaders rely on these highly skilled and expensive data scientists to help them change their respective markets by building predictive capabilities into their products and workflows, but they often think the change can be led by the data science team alone. Across industry sectors, management and leaders see a gap between the promised and delivered impact of data science projects, and wonder why there is such a noticeable difference.
At the heart of this gap is delay. The longer the time to market for data products, the higher their cost and the greater their risk. The risk increases as data drifts, scope creeps, and requirements grow. To shorten the time to market, lower overhead, and reduce the risk, organizations need a comprehensive understanding of how to build artificial intelligence in a repeatable fashion. In other words, organizations need to understand how to operationalize AI. …
While adopting AI is an important priority for businesses moving forward, considering its many potential applications, large numbers of organizations are not yet at a stage of their data strategy and digital transformation to even begin developing AI solutions…
Businesses need a systematic approach to operationalize AI, one that takes into consideration the end-to-end data science and AI life cycle. The AI life cycle consists of a sequence of stages that brings together the personas we discussed earlier into a collaborative process….
Many organizations have begun their journey toward adopting AI: they’ve built data science teams, launched projects, and so forth. Of course, not all of these initiatives have delivered. There’s a noticeable gap between the “haves” and “have nots” as AI becomes a matter of competitive advantage. In this report, we promised that you’d learn how to operationalize AI — in other words, learn the important steps to develop a comprehensive understanding across your organization of how to build AI solutions in a repeatable, timely manner to shorten the TTM, lower overhead, and reduce risks. Think of this as a recipe, if you will. The end goal of operationalizing AI is to establish an AI Center of Excellence and move forward as a unified organization on that journey.”
John Thomas, Paco Nathan, and I bring years of consulting and industry experience to bear in the broader data science and software industry. John and Paco are also both regular presenters at conferences world wide, and looked to by organizations for their expertise and best practices for success in the field of artificial intelligence.
If you’re curious about how the structural changes we talk about in our report could benefit you, consider signing up for the IBM Data Science Community to stay informed of when our report goes live.
