Tag Archives: artifical intelligence

An Inspiring Ice-T, the rise of AI, and other CMSWire CONNECT observations.

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“Ice-T? Why would they have Ice-T at a conference dedicated to customer experience?” Well by the conclusion of my two days at the CMSWire CONNECT conference in Austin last week I had my answer.

But more on Ice-T later.

First off a nod of thanks to my CMSWire editors Dom Nicastro and Siobhan Fagan for arranging for me to attend the conference as a CMSWire contributor. It was great to spend time in person with people I’ve been writing for over the last six years. It was also an excellent opportunity to meet other members of the CMSWire team too. 

Early encounter with AI personas

My two days started off with attending a very thought-provoking Breakfast Briefing from Erin Reilly of the University of Texas on The Rise of Virtual Beings and how they are transforming the customer experience. I must admit I hadn’t given much thought to the use of three-dimensional avatars beyond gaming applications, but her examples of AI-driven personas certainly gave me pause to think about how the digital landscape is continuing to evolve.

Metrics of CX – data and experience

In his discussion on The Customer of the Future, the University of Texas’s Art Markman discussed the application of cognitive psychology to customer behavior and how we measure it. One point that really caught my attention was the observation that

 “If we spend time just looking at data we start to think that every interaction is a digital experience. We need to look beyond that and embrace the real-life experiences and engagements.”

This really resonated as I’ve had several conversations recently about the relative importance of quantitative and qualitative metrics when it comes to determining the quality of content-driven interactions. This drove home that both are equally important. As one later speaker put it, and I’m afraid I missed taking a note of who it was, it’s no good knowing the ‘What’ if we don’t know the ‘Why.’

Katrina Taylor of LuLu Lemon also summed it up nicely in her excellent presentation on Human-Centered Design for Omni-Channel Delivery when she stated:

“You can go through all the data in the world, but you will learn way more in a 30-minute conversation with those on the front line who interface with customers.”

Intelligent content drives personalization

I must admit my heart gave a little jump to hear Matthew Shaeffer from REI talk about the need for intelligent content in his talk on Modernizing the CX Stack. His observation that Intelligent CX  needs to be a series of uniquely assembled interactions driven by content that is structurally rich, and semantically categorized was great to hear. Engineering content in such a way is key to delivering the granular levels of personalized interactions most companies are looking to achieve, and it was great to hear of a major retailer adopting this approach.

Tarunam Verma from Lowes made a smart observation during his presentation on Hyper-Personalization that what we think of as personalization isn’t just about applying technology, in reality, it’s a mix of culture, mindset, and the technology.

Is AI really Augmented Intelligence rather than Artificial Intelligence?

The second day of the conference had a strong theme around the impact of Artificial Intelligence (AI) and Machine Learning (ML) with some excellent observations and talking points discussed throughout the day. Here’s a snapshot of some of the ones that caught my attention:

  • “Use AI in support of creativity not instead of creativity” – Karna Crawford (ex-Ford)
  • “AI is most useful currently as a back-office application that detects operational inefficiencies.”  / “There are two waves of AI: (1) recommendation engines – which are established, and (2) generative – which we are still trying to figure out.” / “Don’t implement AI just for the sake of it, know what problem you are trying to solve. / AI requires us to rethink how we do things.” – Daniel Wu (J.P. Morgan)
  • “Think of AI as ‘Augmented Intelligence” that helps us do our tasks better, not ‘Artificial Intelligence’ that will replace us.” – Raj Krishan (Microsoft).

One of the best questions of the day came from CMSWire facilitator Kate Cox who posed her panelists a pretty philosophical question:

“If I use AI to craft an email and you use AI to read it, are we actually communicating?”

Ice-T on Walls and Boats

Which brings us back to Ice-T as the conference closing keynote. I wasna sure what a former gangster, turned rapper, turned actor would have to say that would be relevant to an audience full of technologists. In fact, I was in two minds about staying, thinking I’d leave a bit early to get ahead of the Austin downtown Friday traffic exodus; but I’m glad I did as he delivered one of the best conference keynotes I’ve seen.

It was entertaining, full of amazing stories, and above all an inspirational discussion on handling change. Here are just a few of his observations that I jotted down:

  • “There are walls – things that you can’t change – and obstacles that look like walls. Get over the obstacles by talking to people that have already got over them. But then make sure to put in the work that they put in.”
  • “Don’t ever get annoyed at the lack of results from the work you didn’t do.”
  • “Anything you do you bring your perspective to it. That’s your value. Make it your own thing.”
  • “Don’t complain, just figure stuff out.”
  • “Take opportunity when it turns up. A lot of times the opportunity is right in front of you. Just get in that boat, at least for long enough to say ‘I don’t like it.’ If you don’t you’ll never know.”

And if the CONNECT conference was one thing, it was a great opportunity to learn from, meet, and network with a whole raft of new people. Thanks to all I chatted with be it after presentations, at vendor booths, or over coffee or meal breaks.

Here’s to getting in the boat.

AI’s Missing Ingredient – Intelligent Content

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My Saturday mornings used to be full of artificial intelligence (AI). Thanks to the TV shows I watched and the comics and books I read, I grew up expecting to live in a world of robots that could think and talk, vehicles of all sizes that would whisk me off to far-away destinations with no need for drivers or pilots, and computers that would respond to voice commands and know the answer to just about everything.

I may not yet have that robot butler, and my first experience with a self-driving car left me more apprehensive than impressed, but in other ways artificial intelligence is now part of my everyday existence, and in ways that I don’t even think about.

One of the first things I do each morning is ask Siri for the day’s weather forecast and then check to make sure that my Nest thermostat is reacting accordingly. During the day, Pandora’s predictive analytics choose my music, and in the evening Netflix serves up my favorite shows and movies. My books arrive courtesy of Amazon, and there’s a fair chance that some of those purchases were driven by recommendations generated via AI.

And now everyday I see several posts about content generated by the AI driven chatbot ChapGT (most of which seems very repetitive to me), while my artist friends debate the ethics of AI generated art (or is it even art at all).

It seems to me that we are on the edge of a potential leap forward in the application of AI, or perhaps more accurately we are making noticeable strides in the application of Machine Learning (ML) rather than true AI.

Outdated practices hampers AI advances

What we have today is just a small representation of the promise of AI, and that promise has not yet been realized.

Many companies and organizations still use older technology and systems that get in the way of a truly seamless AI customer experience. When the systems we already have don’t interact, and companies continue to build point-solution silos, duplicate processes across business units, or fail to take a holistic view of their data, content, and technology assets, then AI systems will continue to pull from a restricted set of information.

Over the past several years, as I have talked and worked with companies that are pursuing AI initiatives, I have noticed that the majority of those projects fail for a common reason; AI needs intelligent content. It may not be the only reason, but it’s definitely a common denominator.

AI needs intelligent content

No artificial intelligence proof of concept, pilot program, or full implementation will scale without the fuel that connects systems to users — content. And not just any content, but the right content at the right time to answer a question or move through a process. AI can help automate mundane tasks and free up humans to be more creative, but it needs the underpinning of data in context — and that is content, specifically content that is intelligent. According to Ann Rockley and Charles Cooper, intelligent content is “content that’s structurally rich and semantically categorized and therefore automatically discoverable, reusable, reconfigurable, and adaptable.” [Ann Rockley and Charles Cooper: Managing Enterprise Content: A Unified Content Strategy, Berkeley: New Riders, 2012]

The way we deliver and interact with content is changing. It used to be good enough to create large monolithic pieces of content: manuals, white papers, print brochures, etc. and publish them in either a traditional broadcast model or a passive mode. We would then hope that, in the best case, we could drive our customers to find our content or, in the worst case, that whoever needed it would stumbled across it via search or navigation.

With the rise of new delivery channels and AI-driven algorithms, that has changed. We no longer want to just consume content, we want to have conversations with it. The broadcast model has changed to an invoke-and-respond model. To meet the needs of the new delivery models like AI, our content needs to be active and delivered proactively. We need to build intelligent content that supports an advanced publishing process that leverages data and metadata, coordinates content efforts across departmental silos, and makes smart use of technology, including, increasingly, artificial intelligence and machine learning.

In addition to Rockley and Cooper’s definition of intelligent content, our content should also be modular, coherent, self-aware, and quantum. Here are definitions of those four characteristics:

  • Modular: existing in smaller, self-contained units of information that address single topics.
  • Coherent: defined, described, and managed through a common content model so that it can be moved across systems.
  • Self-Aware: connected with semantics, taxonomy, structure, and context.
  • Quantum: made up of content segments that can exist in multiple states and systems at the same time.

Intelligent content with a common content and semantics model that allows systems to talk the same language when moving content across silos may be the key to unlocking the technology disconnect that is holding AI back from even greater acceptance.

Machine Learning Isn’t Rocket Science

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Take two astrophysicists, an Apollo engineer, a guy who designed parts of the International Space Station, a professor of robotics, and a random science fiction writer, and what do you have? It sounds like a dream sequence from the TV show, “The Big Bang Theory,” or the start of a science nerd joke. In fact, it was the make-up of a talk panel at a recent science fiction convention where I was one of the guests. The panel was ostensibly meant to be a retrospective look back at the days of Apollo, but like many such conversations, it soon turned to thinking about the future, which led to the subject of machine learning (ML) driven artificial intelligence (AI) and its current capabilities.

I expected an enthusiastic discourse, and so I was surprised when most of these actual rocket scientists seemed more ambivalent about the technology and its potential impacts.

A couple of observations caught my attention enough to tweet them out at the time:

“ML is great at recognizing patterns but not much else.”

“ML assumes tomorrow is going to be the same as today.”

Yet it seems these technologies are being received more enthusiastically elsewhere. Nearly every customer experience discussion and the majority of CX projects my team is engaged in these days includes some mention of machine learning and artificial intelligence (and often the two are used synonymously although they are different). Which got me thinking, how do the somewhat downbeat observations of a panel of space experts play into the world of customer data, and the ways we try to infer context from it?

‘ML Is Great at Recognizing Patterns but Not Much Else’

Machine learning is usually defined as “a set of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions.” It’s a subset of artificial intelligence that relies on patterns and inference to drive conclusions. In other words, as the scientists observed, it’s great at doing what it is meant to: Pattern recognition.

That means it can see what is happening in a data set, but not why it’s happening. That still (at the moment anyway) needs human interaction to derive context based on experience, knowledge, and a degree of intuition.

Machine learning can greatly reduce the workload and automate the process of recognizing patterns of behavior in large sets of customer data, but it is not a magic panacea for developing an understanding of why customers do what they do.

‘ML Assumes Tomorrow Is Going to Be the Same as Today’

The data we get from machine learning is a reflection of what happened the day the data was captured. For the purpose of pattern matching, there is an underlying assumption that the next set of data is going to be similar enough for the patterns and models it recognized to still be applicable.

Machine learning is not a predictive tool. It is a great way to analyze a lot of data and an efficient way to learn about repetitive behavior. But that’s it. The danger can be we take that baseline and believe that is how things will always be. Our customers acted that way yesterday, so they will act the same way tomorrow. If that was truly the case, to paraphrase Henry Ford’s observation, we’d still be riding horses. ML does not take into account the impact of disruptive social or technological influences. Overreliance on technologies like ML without understanding their role in developing a broader understanding of our customers can be just as much a blocker to delivering a good customer experience as any older system or technology.

We’ve Got a Long Way to Go With Machine Learning

When my wife and I get into my car on a Saturday morning, the ML system connected to my phone that analyzes my movements assumes we are heading for our favorite local diner. While that’s true around 80% of the time, on the odd weekend we head off in another direction, and the phone and GPS literally get lost for a while.

We have a long way to go (both figuratively and literally) with machine learning before it drives a true artificial intelligence-driven customer experience.