Everything still to play for with AI in its infancy

Ginni Rometty, head of IBM, has a phrase that describes how Big Blue’s customers are applying the tech world’s most powerful new tools, such as AI: “Random acts of digital”.

* They are anxious to use a technology that promises to extract huge business value out of their data, but she characterises many of the projects they are taking on as hit and miss. They tend to start with an isolated data set or use case — like streamlining interactions with a particular group of customers. They are not tied into a company’s deeper systems, data or workflow, limiting their impact.

Andrew Moore, the new head of AI for Google’s cloud business, has a different way of describing it: “Artisanal AI”. It takes a lot of work to build AI systems that work well in particular situations. Expertise and experience to prepare a data set and “tune” the systems is vital, making the availability of specialised human brain power a key limiting factor.

The state of the art in how businesses are using artificial intelligence is just that: an art. The tools and techniques needed to build robust “production” systems for the new AI economy are still in development. To have a real effect at scale, a deeper level of standardisation and automation is needed.

All of this highlights an important fact. Strip away the gee-whizz research that hogs many of the headlines (a computer that can beat humans at Go!) and the technology is at a rudimentary stage.

Coming from completely different ends of the enterprise technology spectrum, the trajectories of Google and IBM highlight what is at stake — and the extent to which this field is still wide open.

Google comes from a world of “if you build it, they will come”. Two decades ago, it released what was clearly the best internet search engine. Once it found a business model that fitted the service — keyword advertising — there was little friction to its adoption.

To an extent, the rise of software as a service — online business apps such as Salesforce’s customer relationship management tool — have brought a similar approach to business technology. But beyond this “consumerisation” of IT, which has put easy-to-use tools into more workers’ hands, overhauling a company’s internal systems and processes takes a lot of heavy lifting.

By common agreement in the artificial intelligence world, Google is on the cutting edge of research. It has tools for things such as image recognition — available to customers through APIs. It has also been working hard on standardisation and automation with Auto ML, a set of tools to turn machine learning into a more streamlined process.

But true enterprise software companies start from a different position. They try to develop a deep understanding of their customers’ problems and needs, then adapt their technology to make it useful. For Google and companies like it, this “outside in” approach represents a huge cultural change.

In the clearest sign it understands how much is at stake, Google brought in Thomas Kurian, a top-ranking Oracle executive, late last year to run its cloud business. Speaking publicly for the first time this week, Mr Kurian promised that Google would accelerate its hiring to build a bigger salesforce and get deeper into its customers’ businesses.

IBM, by contrast, already knows a lot about its customers’ businesses, and has a huge services operation to handle complex IT implementations. It has also been working on this for a while. Its most notable attempt to push AI into the business mainstream, Watson (a computer that beats humans at question-and-answer games), is eight years old.

Watson, however, turned out to be a great demonstration of a set of AI capabilities, rather than a coherent strategy for making AI usable. 

IBM has been working hard recently to make up for lost time. Its latest adaptation of the technology, announced this week, is Watson Anywhere — a way to run its AI on the computing clouds of different companies such as Amazon, Microsoft and Google, meaning customers can apply it to their data wherever they are stored. 

This points to a wider front in IBM’s campaign to make itself more relevant to its customers in the cloud-first world that is emerging. Rather than compete head-on with the new super-clouds, IBM is hoping to become the digital Switzerland. 

This is a message that should resonate deeply. Big users of IT have always been wary of being locked into buying from dominant suppliers. Also, for many companies, Amazon and Google have come to look like potential competitors as they push out from the worlds of online shopping and advertising.

But if IBM has found the right message, it faces searching questions about its ability to execute — as the hit-and-miss implementation of Watson demonstrates. Operating seamlessly in the new world of multi-clouds presents a deep engineering challenge. Among the risks IBM has now taken on is the huge acquisition of open-source company Red Hat.

With the future of AI in business up for grabs, however, this is a clearly a time for the bold bet.

*This article has been amended to correct Ms Rometty’s quote This email address is being protected from spambots. You need JavaScript enabled to view it.

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