Thomas Davenport Quotes

DDT stood for Dangerous Darrell Thomas. Thomas had given himself the name when he was riding with a motorcycle club and was interviewed for a public radio magazine. The magazine writer got it wrong, though, and referred to him as TDT--Terrible Darrell Thompson--which lost something of its intent when expressed as initials; and since the writer got the last name wrong, too, Thomas never again trusted the media.

The moment a realm of intellectual activity is codifiable, it ceases to be uniquely human.

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.

In an attempt to develop a tool to help organizations begin to identify these errors, the Institute for Healthcare Improvement (IHI) developed a global trigger tool (GTT).9 Briefly, the GTT provides a standard methodology for reviewing patient records for triggers, or indicators, of potential adverse events.

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.

That was one of the reasons why Memorial Sloan Kettering Cancer Center decided to work with Watson—to make widely available the level of knowledge of the hospital’s best oncologists to the world at large.

Thomas Davenport, author of The Attention Economy, wrote: “Understanding and managing attention is now the single most important determinant of business success.

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.

As the sun fell over the Davenport Drive-In and the previews lit up the giant screen, Annalisa and Thomas laughed hysterically at the people scrambling out of the trunks of cars. They’d do anything to avoid the two-dollar admission. Annalisa recalled it all from Bangor: the swath of light shooting from the projector to the screen up front, the fogged-up cars with kids making out in the back seat, the savory smell of buttery popcorn wafting through the air. With

One other problem is that too many people—and vendors in particular—are already using big data to mean any use of analytics, or in extreme cases even reporting and conventional business intelligence.

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

The availability of all this data means that virtually every business or organizational activity can be viewed as a big data problem or initiative. Manufacturing, in which most machines already have one or more microprocessors, is increasingly a big-data environment. Consumer marketing, with myriad customer touchpoints and clickstreams, is already a big data problem. Google has even described its self-driving car as a big-data project.

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.

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.

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

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Some organizations are attempting to predict the nature of future jobs and the skills they will require. This is, of course, difficult or impossible to do with any precision.

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.

In Europe, a consortium of microelectronics companies, the Euro Pact for Skills, is devoting €2 billion to train current and future employees on electronic components and systems.

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. Education

It takes about twelve years for a pharmaceutical firm to research, develop, test, and launch a product. Several firms, including Pfizer, Novartis, and Celgene, are working with IBM Watson to try to identify and bring new drugs to market faster.

UPS are replacing fixed daily routes with dynamic ones adjusted in real time for weather and traffic—again with machine learning. Only cognitive technologies can handle all the necessary data. And at some point supply chains may be powered by autonomously driven trucks, which will bring enormous changes to that domain.

The beginning of love is to let those we love be perfectly themselves, and not to twist them to fit our own image. Otherwise we love only the reflection of ourselves we find in them. ~Thomas Merton

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.