Amplify showed that Watson is out of the lab

Amplify showed that Watson is out of the lab

I’m back from IBM Amplify 2017 with renewed enthusiasm for Watson Customer Engagement. I left Las Vegas with a sense of purpose; feeling IBM has real direction with A.I and cognitive computing in Watson. Its nailed its transition from on-premise to SaaS and its proposition is strong and clear. Watson is out of the lab and into the marketplace, ready to be discovered…

A.I and Cognitive is left, right and centre     

There are many compelling reasons to use Watson but what clearly came out of out Amplify last week was that A.I and cognitive is left, right and centre for IBM.

I saw some really dynamic presentations from Harriet Green, Richard Hearn and Will Smith, who all spoke fervently about redefining customer engagement in the cognitive era and the opportunities that Watson will bring to connect with customers as individuals.

Ginni Rometty’s address also convincingly set out how bringing cognitive capabilities together with the cloud will enable new innovation to solve problems and create new marketing solutions.

The new wave of people at IBM has led to a change in culture, with those at the heart of the organisation driving a real understanding of A.I and cognitive.

Watson heralds a new era     

With IBM Watson Customer Engagement, cognitive is now accessible through its simplified product range and easy to understand language.

There’s a new clarity with Watson. Its highly structured and well-defined platform, its user-centric design, smooth integration and cognitive expertise is just waiting to be discovered.

The challenge now is for businesses to understand how A.I and cognitive can be practically applied.

Grant Williams 

 

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It’s time to get practical with A.I and cognitive computing

It’s time to get practical with AI and cognitive computing

Everyone is excited about A.I and cognitive computing – the boundless opportunities, the raft of applications in development and the promise of a new era ahead for marketers. I’m excited too, especially by how these technologies are already being used, but I see many businesses falling into a trap. In the rush to embrace A.I and cognitive computing, many of the practicalities around its implementation are being overlooked.

Truly understanding the technology

As I wrote about last week in the Huffington Post  a large part of harnessing the opportunities cognitive computing and A.I can bring is in truly understanding how these technologies work and how they can benefit an organisation. There’s still a lot of confusion around this.

Many organisations mix up predictive systems and cognitive systems, for example. Predictive marketing is based on analysing huge amounts of data and automating responses. True cognitive computing is teaching a system to think like a person and learn as you train it. It can take data (which does not have to be personal) and learn from this. This, in conjunction with A.I technology opens up a huge range of new ways to reach and interact with customers.

Importantly, although cognitive computing is designed to learn and run independently, it will always work best in partnership with people. For example, cognitive technology can run automated tasks such as reporting or email campaigns, freeing up people to focus on creativity and delivering better customer experiences, such as Augmented Intelligence.

Don’t be seduced by gimmicks 

Whilst organisations are keen to stay one step ahead of their competitors, they do need to look beyond a ‘gimmick-led’ application of these technologies and instead investigate how it can be applied to actively improve personalised customer experience.

To do this, organisations need to step back and start with the customer. Understand how customers are interacting with a brand and what kind of experience they are looking for. People don’t necessarily want a relationship with a brand, they just want a good experience.

The North Face is one example of where cognitive computing is being practically applied to deliver this kind of experience. Users visiting The North Face website can have a similar experience online as in-store, thanks to intelligent natural language processing technology that helps customers choose a jacket by asking a series of questions and learning from the answers supplied. Powered by IBM Watson cognitive computing technology together with Fluid XPS the retailer can provide customers with outerwear suggestions tailored to their needs, creating a more engaging, relevant and personalised shopping experience.

Getting your house in order

Perhaps more fundamentally though, businesses first need to get their own houses in order before embarking on implementing new technologies such as cognitive or A.I.

Innovating and pushing the boundaries of what is possible through the use of exciting technologies is of course great. However, in order to gain value from groundbreaking technology and turn it in to something that will deliver significant improvement to their customers, it is vital that organisations strike the right balance. As Kevin Kelly, author and founder of Wired famously said “perfect what you know”.

 

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Ask the experts report

 Ask the experts report

Innovating and pushing the boundaries of what is possible are part of the very fabric of the technology industry. There will always be new and exciting technologies and trends to explore. This is entirely as it should be. However, in order to gain value from groundbreaking technology and turn it in to something that will deliver significant improvement to their customers, it is vital that organisations strike the right balance.

By all means follow the latest predictions and set aside time and budget to innovate, but make sure the basic building blocks are in place too.

Over the last few weeks we have been sharing excerpts from our latest ‘Ask the Experts’ report, which outlines the key marketing and data technology challenges and opportunities facing organisations at the moment.

These can be summarised as:

  1. Getting the basics right

When implemented and integrated correctly, marketing and data technologies have the potential to drive digital transformation, enable business intelligence, allow organisations to become truly data led and ultimately transform customer experience for the better.

All too often, we see organisations either rushing to buy marketing and data technology, or investing in new technology, which then does not deliver on its promise or expectation.

Businesses need to ensure they have the basic building blocks in place in order to get real benefit from any technology they purchase. What is often overlooked is the hugely important role data plays. Nearly every new trend such as A.I, cognitive computing and IoT has data at its core. Sure, data is not as headline grabbing as the above-mentioned technologies, but none of them are possible without access to, and good integration between accurate and relevant data.

  1. Real-time decision making finally gets real

While there was a huge amount of noise about real-time decision-making and real-time next best action marketing a few years ago, we haven’t as yet seen significant practical application of this technology.

This is set to change from 2017 onwards. Many organisations looked into or acquired technology to facilitate real-time when it first emerged as a leading trend, but it is only now that many are actually practically applying it.

  1. Taking steps towards cognitive computing

The concept of cognitive computing and A.I has been much discussed recently, in the same way that real time marketing was a few years ago. While there have been a limited amount of practical applications of this technology to date, there is no doubt that the concept is set to dominate the landscape for some time.

The next few years will see organisations start to get to grips with what cognitive computing can offer. There are still fundamental kinks to be worked out, more fundamentally, though, businesses need to look beyond a ‘gimmick-led’ application of these technologies and instead investigate how it can be applied to actively improve personalised customer experience.

  1. The growing need for data management and governance

Data management is a huge commodity. Proponents of data value management have long urged organisations to see data as a corporate asset and they are right.

Just like any asset organisations should attach cost and value to their data.

Yet how many organisations are actually doing any of this?  Only a small minority of market leaders.

The majority only considers data in this way when a specific requirement rears its head.  Often this will be a regulatory or technology driven change.

The temptation to wait until a project demands better data management is commonplace.  But project thinking can mean data governance and lifecycle management processes happen in a ‘siloed’ fashion.

  1. Getting your digital estate in order

Organisations are still failing to fully understand their digital estates and the systems they already have. Many are fairly digitally mature, with estates that have grown at a rapid pace. Due to the particularly high turn-over in senior marketing roles, coupled with increasing marketing technology spend, businesses are likely to have multiple systems in place, which are not being utilised or integrated properly.

These ‘Frankenstacks’ of disconnected technology have developed for a number of reasons, primarily due to the fact that organisations have been working in silos for years. This creates a monster of parts, all probably very good in their own area but as a combination stitched and patched together and not always serving the common good.

However, in this age of the customer, consumers expect – in fact demand – a seamless, joined up, personalised experience. Something that is difficult to deliver in a disjointed digital estate.

By closely examining current marketing and data architecture, and the way systems, tools and data presently connect (or fail to connect as the case may be), organisations can gather a clearer idea of how to effectively join up and better manage a digital estate.

 

To read the Ask the Experts Report in full request your copy.

Talk to us if you want to learn more.

Taking steps towards cognitive computing

The concept of cognitive computing and A.I has been much discussed recently, in the same way that real time marketing was a few years ago. While there have been a limited number of practical applications of this technology to date, there is no doubt that the concept is set to dominate the landscape for some time. All the big players such as Adobe, Salesforce and IBM are vying to take the lead here, with IBM’s Watson in particular making waves in the industry.

The next few years will see organisations start to get to grips with what cognitive computing can offer. While there is much fascination with the potential for cognitive, there is still an element of nervousness from many organisations, especially when it comes to A.I. This is not unfounded, as A.I has not yet reached the point where it can run without careful human monitoring.

There are still fundamentals to be worked out to achieve true machine learning where the machine is fully responding and recalculating on changing inputs without any programming from a human party.

More fundamentally, though, businesses need to look beyond a ‘gimmick-led’ application of these technologies and instead investigate how it can be applied to actively improve personalised customer experience.

For example, this could take the form of a holiday company knowing that an individual likes to ski, has two children aged six and nine, has been on skiing holidays before in the February half term and favours Italian resorts over French ones, and then drawing information from 1st, 2nd and 3rd party data as well as analysing weather statistics and flight information and then offering appropriate holiday options based on this information.

Again, data is the key here. The more relevant data that is gathered, the more personalised the experience for customers. The importance of having excellent processes in place to capture and manage data is perhaps more significant than ever. As data scientist Bradley Voytek famously said while at Uber: “I don’t need to know everything about everybody. I just need to know a little bit about a whole bunch of people.”

Those that succeed will be the ones who can properly leverage both data and technology to make customers’ lives better.

 

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