Thomas H. Davenport Quotes

the use of statistical process control tools to evaluate variation, correlate root cause, forecast capacity, and anticipate throughput barriers. By measuring incidence of preventable venous

Knowledge workers have high degrees of expertise, education, or experience, and the primary purpose of their jobs involves the creation, distribution or application of knowledge.

One large US bank, for example — an aggressive adopter of AI — has announced a $350 million investment in reskilling related to AI-related job changes, and the bank is being both predictive and granular about the initiative.10 It’s working with researchers from MIT and elsewhere to understand — based on a “suitability for machine learning” (SML) assessment — which skills and jobs are most likely to be replaced by AI.11 The SML analysis will help the bank plan for changes in those jobs and help workers gain the skills they need to succeed in their modified jobs or transition to new ones.

to frame the decision, asking questions about the data and the methodology, working to understand the results, and using them to improve outcomes for your organization.

Attention is focused mental engagement on a particular item of business. Items come into our awareness, we attend to a particular item, and then we decide whether to act.

Another AI-oriented company that has established several ecosystems — also in partnership with Palantir, in which it has made a substantial investment — is SOMPO Holdings, a large insurance and elder care company based in Japan. SOMPO

Similarly, one of the most popular HR tools at GE Digital — an early adopter of AI for manufacturing applications — shows workers which jobs in the company are natural next steps from the ones they have now.12 Employees can look privately at the tool to see possible paths they can follow, skills they may need to acquire, or even positions that are open. This helps employees feel that they have more opportunities and that they have more control over their positions in the company.

Since Rosenthal came up with the recommendation idea, Morgan Stanley has been working on a next-best-action (NBA) system to provide its advisors with financial insights to present to clients.

Business process reengineering encompasses the envisioning of new work strategies, the actual process design activity, and the implementation of the change in all its complex technological, human, and organizational dimensions

We refer to these companies’ all-in approaches in multiple ways — “AI fueled,” “AI powered,” “AI enabled,” etc. The common thread is that they are at the far end of the scale in their spending, planning, strategizing, implementing, and changing with regard to AI technology.

Other firms are working on marketing applications of machine learning that increase customer engagement. Macy’s, for example, is working with both IBM’s Watson and Cognitive Scale, an Austin-based AI vendor, to improve personalization and engagement on its website and mobile app.

James March, a Stanford-based social scientist... is much more a gurus’ guru than a guru to the general public. He finished second on the gurus’ gurus list and 48th on the original list.

In definitional terms, a process is simply a structured, measured set of activities designed to produce a specific output for a particular customer or market. It implies a strong emphasis on how work is done within an organization, in contrast to a product focus’s emphasis on what. A process is thus a specific ordering of work activities across time and space, with a beginning and an end, and clearly defined inputs and outputs: a structure for action.

We believe that every large organization — and certainly those that are or aspire to be AI first — should designate smart people to follow AI technology trends, try out new technologies, and import them when they seem to fit the organization’s needs. These people don’t need to be fantastic data scientists or AI engineers, but they do need to understand the key technologies in AI and how they support use cases and business needs.

The businesses and organizations that succeed with AI will be those that invest steadily, rise above the hype, make a good match between their business problems and the capabilities of AI, and take the long view.