After taking a year off to more fully integrate its artificial intelligence (AI) with its data and analytics, IBM is back with what it hopes is a more accessible and marketable AI product.
When it comes to chasing the market’s unrequited love affair with AI, IBM has certainly done its part in generating hype for its multi-billion dollar investment in IBM Watson since it was first introduced.
That hype, which has had some rather lofty goals – such as identifying and diagnosing cancer – have not fully panned out, with some early adopters scaling back or halting operations altogether due to concerns over cost and efficacy.
This has given AI rivals Microsoft, Google, Amazon and others ample room to operate with a more modest scope of concern, namely in delivering AI tools that lessen the demand for data science expertise.
Recognising that it has perhaps overshot the AI value proposition, at least in terms of hype versus reality, IBM has both drawn back from its earlier, over-aggressive use of the Watson brand and rejigged its internal organisation to better blend AI with the data that drives it.
IBM is now back with a new marketing message, Watson Anywhere, an enterprise-ready AI platform. which it intends to use to take its rivals head-on.
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By GlobalDataThe company took this message to its annual IBM Data and AI Forum in Miami, Florida last week, where it was keen to portray IBM Watson – not as a magic black box capable of winning US TV game shows (Jeopardy! in 2011), offering to discover new drugs or suggesting your next favourite beer (all of which IBM Watson has attempted in the past).
IBM has streamlined its product offerings into three key areas:
a) Tools (libraries, development environments, etc.), which customers can use to build their own AI solutions;
b) Pre-packaged AI solutions, which can be used to target specific AI outcomes;
c) Embedded AI ‘features’, which permeate IBM’s broader software portfolio.
IBM has also identified three concerns for enterprise customers:
a) trust in outcomes;
b) having unsuitable data; and
c) inadequate skills.
Regarding the problem of trust, IBM has introduced IBM AI OpenScale, which allows users to understand how AI arrives at a given decision, identifying and rooting out bias in real-time, and ensuring that AI routines are fully auditable (key for regulatory concerns).
To tackle unsuitable data, the company recently released AutoAI, a part of IBM Watson Studio, which quite literally automates many of the steps required to collect, prepare and pre-process data for use within machine learning (ML) data models.
Lastly, to bridge the AI skills gap, IBM already offers several pre-built solutions, some industry-specific and others, such as Watson Discovery and Watson Assistant, are more broadly applicable.
IBM’s secret weapon to make IBM Watson more accessible doesn’t lie in the technology, but rather rests with a small team of data professionals, the IBM Data Science Elite Team.
The team offers free professional consultations – ranging anywhere between 30 minutes meetings to six-week on-premise engagements. The elite squad also has a paid option for 10-plus weeks co-development efforts. IBM’s goal is to market the value of IBM Watson and to speed up AI adoption.
IBM is confident that the proper application of its AI solutions can help businesses turnaround abandoned projects. If IBM can turn even a few potentially lost projects around at its own expense, those customers and the industry as a whole will realise the value of its AI proposition is not found solely within the software but also with its human expertise and insight.
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