AI News, artificial intelligence (AI) · Forrester artificial intelligence

Valuing the Artificial Intelligence Market, Graphs and Predictions

In order to put together an executive brief for market size and projected growth of AI, I’ve molded this article around (a) AI-related industry market research forecasts, and (b) a limited number of reputable research sources for further insight into AI valuation and forecasting, in addition to select and relevant quotes.

The goal of this article is do provide a short consensus on well-researched projections of AI’s growth and market value in the coming decade, and to (as always) provide amble references for further exploration for those of you who aim to go deeper. We’ve aimed to stick to sources whose reputation rests on their objectivity, rather than on excited statements of companies whose incentive is to see the future their way (such as IBM’s CEO claiming a potential $2 trillion dollar market for “cognitive computing”).

(The following profiles and forecasts are updated on a quarterly basis) Select quotes: In 2017, these technologies will increase businesses’ access to data, broaden the types of data that can be analyzed, and raise the level of sophistication of the resulting insight.

The vast majority of firms believe that having an organizational model that supports analytics is critical to breaking down the silos of customer knowledge that exist throughout the enterprise…Enterprises are starting to show signs of elevating the priority of, and investment in, initiatives to get rid of existing silos.

3) Leading CI practices will be the poster child for business transformation Select quotes: Tractica forecasts that the revenue generated from the direct and indirect application of AI software is estimated to grow from $643.7 million in 2016 to $36.8 billion by 2025.

The chart below shows Tractica’s top 10 AI use cases in terms of revenue in 2025: Tractica has assumed a somewhat conservative adoption of AI in the hedge fund and investment community, with an assumption that roughly 50% of the hedge fund assets traded by 2025 will be AI-driven.

Spiderbook’s current data visualization of companies investing in AI: Study context: Accenture, in association with Frontier Economics, modeled the potential impact of AI for 12 developed economies that together generate more than 50 percent of the world’s economic output.

and To fulfill the promise of AI as a new factor of production that can reignite economic growth, relevant stakeholders must be thoroughly prepared—intellectually, technologically, politically, ethically, socially—to address the challenges that arise as artificial intelligence becomes more integrated in our lives.

“The average business expects to spend $8 million on big data-related initiatives this year,” according to the Kearney report, which also says each IT job created in the process of upgrading will create three additional jobs outside IT.

Forrester says that in 2016, machine learning will begin to replace manual data wrangling and data governance dirty work, and vendors will market these solutions as a way to make data ingestion, preparation, and discovery quicker.

Autonomous Agents and Things Machine learning gives rise to a spectrum of smart machine implementations — including robots, autonomous vehicles, virtual personal assistants (VPAs) and smart advisors — that act in an autonomous (or at least semiautonomous) manner.

Select quotes: 1)    By 2018, 20 percent of business content will be authored by machines. Technologies with the ability to proactively assemble and deliver information through automated composition engines are fostering a movement from human- to machine-generated business content…

2)    By 2018, six billion connected things will be requesting support. In the era of digital business, when physical and digital lines are increasingly blurred, enterprises will need to begin viewing things as customers of services — and to treat them accordingly…

6)    By 2018, 45 percent of the fastest-growing companies will have fewer employees than instances of smart machines. Gartner believes the initial group of companies that will leverage smart machine technologies most rapidly and effectively will be startups and other newer companies…

There are likely to be leaps and bounds in the next decade in gleaning insights from “unstructured data”, while applying predictive analytics and building business models is a more oft-used approach to implementing machine learning and data mining technologies at present.

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