Thomas H. Davenport Quotes

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.

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.

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.

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.

Every decade or so, the business world invents another term for how it extracts managerial and decision-making value from computerized data. In the 1970s the favored term was “decision support systems,” accurately reflecting the importance of a decision-centered approach to data analysis. In the early 80s, “executive information systems” was the preferred nomenclature, which addressed the use of these systems by senior managers. Later in that decade, emphasis shifted to the more technical-sounding “online analytical processing,” or OLAP. The 90s saw the rise of “business intelligence” as a descriptor. In the middle of 2000’s first decade, “analytics” began to come into favor, at least for the more statistical and mathematical forms of data analysis.

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.

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.

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

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.

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.

Ultimately, knowledge worker performance comes down to the behaviors of individual knowledge workers.

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.

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.

Peter Drucker wasn’t exaggerating when he noted that the fate of advanced economies depends on making knowledge workers more productive. There is no business or economic issue that is more important to our long-term competitiveness and standard of living.

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