Retrofitting AI – key adoption issues in the enterprise 2019-2020

Retrofitting AI – key adoption issues in the enterprise 2019-2020

AI technology has moved beyond the hype phase, but short-term adoption of AI in organizations will primarily come through third-party software and relatively straightforward application of Machine Learning, even though many organizations are not yet ready for the latter.

The 2018 AI hype machine was as close to jumping the shark as anything I’ve seen over more than 30 years understanding this field of technology innovation.

Machine Learning holds the greatest promise yet much needs to happen before firms see a genuine business value stream, Even so, there are excellent opportunities for organizations retrofitting AI functions into their own applications to boost speed, accuracy, and productivity.

Caution: AI cuts to the core of human contribution and will need vigilant leadership to prevent disorganization, distortion and dysfunction. It is just as likely that human experts in select fields such as finance, underwriting, claims processing and credit, for example, will be co-opted by AI adoption as those performing manual processes.

Artificial intelligence (AI) is old technology, with new implementations. However, the advent of increasingly parallel programming models and unprecedentedly scalable hardware, coupled with the opportunity to pursue significant new business value served to make AI 2019 tech’s glittering fashion statement. As executives consider adding AI to their business system portfolio over the next 24 months, they must understand the following:

  • Not everything called AI is real. Psychologists and neuroscientists are still trying to understand what human intelligence is, so “intelligence” in the context of “artificial” and “human” is the same word to describe two different things. Think Paris, France and Paris, Texas. Distinguishing between core AI disciplines and technologies and AI applications that are built from those technologies is important to keep track of AI investments and expected business outcomes (see Figure 1).
  • In 2019, AI can stand for “additive intelligence.” Organizations will find that their existing applications can be enhanced with the application of AI “wrappers,” particularly replacing manual data ingestion, human expert forecasting, and data discovery. It is becoming easier for in-house developers to use AI technology, especially since Amazon AWS, IBM Watson and Microsoft Azure, among others, provide useful API’s for AI algorithms. However, enterprise software providers have far more resources to implement AI capabilities and most AI will be added to business systems through software packages.
  • AI can lead to organizational distortion and dysfunction. AI implementation has a direct effect on the nature of work in organizations. Adjusting to this is never simple. Employees see AI coming and they will push back, either purposely or not.


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