Enterprise artificial intelligence has reached a tipping point. Whatever you call it – machine learning, predictive analytics, artificial intelligence, cognitive computing – this phenomenon is not only dominating our news feeds, but our business conversations also.

It would probably be helpful to start with a baseline definition.

AI is using a machine to understand past behaviour in order to first predict, then potentially alter future behaviour to produce more optimal outcomes.

All the major enterprise software vendors and hundreds of up-and-comers are rolling out AI-powered solutions.

>See also: The beginning of AI in the enterprise

At Dreamforce earlier this year Salesforce announced the launch of Einstein, a set of AI services, Oracle has recently been buying start-ups in the AI space and Google has its long established DeepMind practice.

Perhaps this business revolution is fuelled by the ever-present AI we already experience in our personal lives: Netflix, Amazon, Pandora, Nest … even self-driving cars.

From before we even wake up in the morning, AI is making our personal lives easier through intelligent recommendations and automation.

AI is already so ingrained into our personal lives, it seems that the consumer space is about ten years ahead of the enterprise.

Employers have been slow to bring AI into the workplace, so there is currently a gap in our day (from about 9am – 5pm!) where employees largely exist without the help of AI.

In fact, this just means we are working harder and spending more time on tasks that could (and should) be automated or optimised.

Why has the enterprise been slow to adopt AI?

The reason it has taken so long for AI to permeate your workplace is that most organisations believe AI is all about the algorithms.

But, in fact, the real value lies in the data and the workflow. Meaningful AI requires three crucial elements:

· Algorithms – those that look for patterns in data in order to predict future outcomes.
· Data – information that continually feeds the algorithms, making them smarter and more accurate.
· Applications – software that turns predictions and prescriptions from the algorithms into improved outcomes by integrating into activities and workflows.

>See also: How cloud and AI will form the ‘matrix’ of enterprise innovation

Anyone can build an algorithm, but the real magic of AI, and what sets winning solutions apart, lives in the data. Better data results in better predictions, which ultimately produce better outcomes.

Business-to-consumer (B2C) companies learned this a long time ago.

Their advantage is larger customer bases that generate massive amounts of timely and accurate data. Amazon is a great example of how big data and AI can be applied to the sales industry.

As early as 2005, Amazon had gathered data from as many as 84 million unique shopper profiles – and was making recommendations for people on what they should purchase next.

It got to the point where Amazon had a better idea of what any customer wanted to buy than they did. Having such a huge data set readily available has given Amazon a considerable competitive advantage.

It gives it insights into customer decision-making that competitors simply don’t have, which in turn helps it predict future trends and buy products and services accordingly.

Most business-to-business (B2B) companies struggle to generate enough scale in their own data to make it useful.

The only way to solve this problem is to passively crowdsource anonymous data across a wide variety of companies.

In other words, we can only make AI work in the enterprise by getting businesses working better together.

To achieve the full power of AI, you also need the third layer: applications.

>See also: Artificial intelligence: the problems worth solving

Applications integrate the predictive and prescriptive recommendations from the AI platform into the workflow of employees – aka, bringing it all together.

Without the right applications, you get what we call “No-lift AI.” It may be predictive, but it doesn’t drive real business results because employees simply won’t use it.

When you bring all of these elements together – the algorithms, actionable data, and applications – artificial intelligence can finally start to achieve substantial business impact.
Sourced by Martin Moran, SVP and GM, EMEA, InsideSales.com