In March 2017, Bosch hosted its annual internet-of-things (IoT) conference, Bosch Connected World (BCW), in Berlin. Since last year, the event has doubled in size, attracting 2,500 attendees from businesses and vendors. This jump reflects the growing interest in IoT. The number of attendees, however, also highlights the relative immaturity of IoT compared with bigger technology themes. Despite being smaller than events such as GE’s Minds + Machines or Mobile World Congress, BCW has established itself as a premier IoT event, as it has a very distinct “IoT practitioner” feel to it. We took away some key observations for IoT practitioners from the event:
To succeed in IoT, you must build and participate in open ecosystems. No vendor or end user can plan, build, and run end-to-end IoT operations that address the entire customer life cycle. This message comes through loud and clear at all the IoT events that we attend, be it IBM’s Genius of Things or GE’s Minds + Machines, and it was repeated by all the BCW speakers. The notion of coopetition was tangible, with Bosch emphasizing its partnerships with IBM, Software AG, Amazon, GE, SAP, and many more. Also noticeable was that all ecosystem participants are grappling with what it means for the shape of their business and their relationship with the customer.
A riddle: What's the difference between your content and mashed potatoes?
To the technologies that host and deliver your content, the stuff they deliver may just as well be mashed potatoes as text strings or image files.
Even marketers who spend lots of time tagging content know the process is very fallible, often out of date, and only applicable to a handful of pre-selected contexts.
The technology simply doesn't know what the content's actually about, or how it works. It's just content.
The same applies to marketers across the business.
That great video explainer that got made two years ago during another CMO's tenure?
It may as well be a little portion of mashed potatoes buried under a mountain of other mashed potatoes.
Enough of the metaphor. You get it.
Content intelligence changes all that.
It is technology that helps content understand itself - what it's about, how it speaks, how effective it is at accomplishing certain goals, what emotions it calls to mind, etc.
That may sound funny. It is. But it's not necessarily stranger than spellcheck in your word processor.
Thanks to a built-in dictionary, the processor knows that 'recieve' may not be right, and puts a little red line under it.
Content intelligence goes a bit further, in that it's continuously updating itself.
Iimagine a very smart dictionary that automatically absorbed neologisms and understood word choice given context ("you might want to say 'car' here instead of 'automobile'").
But the principle's the same.
And because content's the coin of the digital realm for all things marketing these days, content intelligence delivers a real kick:
To be blunt, if you miss this event, you’ll be sorry. Sure there are loads of marketing conferences out there, but Forrester’s Forums clear the clutter and help you focus on the issues that matter most to your success. Last year, we told you that we're in a post-digital world now, and that marketing must adapt to new rules. This year, on April 5-7, we'll show you exactly how to do that and more. Whether you’re developing and refining your marketing strategy to engage today’s empowered consumer, or your planning the next investment in your Martech application portfolio, Forrester’s Consumer Marketing Forum will be the smartest investment of time that you’ll make this year. Here’s a just few highlights:
Learn exactly how consumers’ behaviors are changing. Analyst Anjali Lai will share Forrester's Empowered Customer segmentation.
Discover how to avoid the illusion of insights. VP and Research Director Sri Sridharan will show you how to avoid potential pitalls in your question to become and insights-driven business.
Reveal what really matters in Martech and Adtech. VP and Principal Analyst Joe Stanhope will bring clarity to the chaos of an unhealthy technology ecosystem.
Recently, the largest annual get together of the mobile industry, Mobile World Congress (MWC) took place in Barcelona. In my opinion, the biggest themes at MWC in 2017 that are relevant for enterprise customers were the internet of things (IoT), artificial intelligence (AI), platforms, collaboration, and connectivity. These themes underline how mobility is becoming part of the broader digital transformation initiative. I discuss this shift in this separate blog and report. MWC provided several valuable insights for business and technology leaders to align their mobile to their digital strategies:
-> Not everything that claims to be AI is true AI. Many vendors that claimed during MWC to be AI-proficient are in fact able to deliver true machine-learning solutions to generate transformative customer and operational insights. Most solutions that were branded as AI at MWC rely on preprogrammed responses and statistics rather than machine learning.
IBM hosted an artificial intelligent (AI) event at its Munich Watson IoT HQ, where it underlined its claim as a leading global AI and internet-of-things (IoT) platform providers in the enterprise context. AI and the IoT are both very important topics for enterprise users. However, there remains some uncertainty among enterprises regarding the exact benefits that both AI and IoT can generate and how businesses should prepare for the deployment of AI and IoT in their organizations.
One year into the launch of its Munich-based Watson IoT headquarters, IBM invited about one thousand customers to share an update of its AI and IoT activities to date. The IBM “Genius of Things” Summit presented interesting insights for both AI and IoT deployments. It underlined that IBM is clearly one of the leading global AI and IoT platform providers in the enterprise context. Some of the most important insights for me were that:
AI solutions require a partner ecosystem. IBM is well aware of the fact that it cannot provide IoT services on its own. For this reason, IBM is tapping into its existing partner ecosystem. Those partners are not only other vendors. IBM’s ecosystem partnership approach embraces also customers such as Schäffler, Airbus, Vaillant, or Tesco. The event demonstrated how far IBM has matured in living and breathing customer partnerships in the IoT solutions space. For instance, IBM’s cooperation with Visa regarding secure payment experiences for any device connected to the IoT is an example of a new quality of ecosystem partnership.
Artificial intelligence (AI) is real, albiet maturing slowly. You experience it when you talk to Alexa, when you see a creepily-targeted online ad, and when Netxflix turns you on to Stranger Things. Oh yea, and that self-driving car over there is AI super-powered! AI is indeed cool, but many are scared about how it ultimatley may impact society. Stephen Hawking, Elon Musk, and even the Woz warned that "...artificial intelligence can potentially be more dangerous than nuclear war." In a nutshell, they are concerned about AI that may evolve to outsmart humans and kill people - a valid concern. But, I have another more terrifying concern that would likely be an insidious precursor to runaway, killer AI.
Much has been written about how artificial intelligence (AI) will put white-collar workers out of a job eventually. Will robots soon be able to do what programmers do best — i.e., write software programs? Actually, if you are or were a developer, you’ve probably already written or used software programs that can generate other software programs. That’s called code generation; in the past, it was done through “next” generation programming languages (such as a second-, third-, fourth-, or even fifth-generation languages), today are called low code IDEs. But also Java, C and C++ geeks have been turning high level graphical models like UML or BPML into code. But that’s not what I am talking about: I am talking about a robot (or bot) or AI software system that, if given a business requirement in natural language, can write the code to implement it — or even come up with its own idea and write a program for it.
Pure AI is true intelligence that can mimic or exceed the intelligence of human beings. It is still a long way off, if it can even ever be achieved. But what if AI became pure — could perceive, think, act, and even replicate as we do? Look to humanity for the answer. Humanity has been both beautiful and brutal:
The beauty of ingenuity, survival, exploration, art, and kindness.
Artificial Intelligence (AI) is not one big, specific technology. Rather, it is comprised of one or more building block technologies. So, to understand AI, you have to understand each of these nine building block technologies. Now, you could argue that there are more technologies than the ones listed here, but any additional technology can fit under one of these building blocks. This is a follow-on to my post Artificial Intelligence: Fact, Fiction, How Enterprises Can Crush It
Here are the nine pragmatic AI technology building blocks that enterprises can leverage now:
■ Knowledge engineering. Knowledge engineering is a process to understand and then represent human knowledge in data structures, semantic models, and heuristics (rules). AD&D pros can embed this engineered knowledge in applications to solve complex problems that are generally associated with human expertise. For example, large insurers have used knowledge engineering to represent and embed the expertise of claims adjusters to automate the adjudication process. IBM Watson Health uses engineered knowledge in combination with a corpus of information that includes over 290 medical journals, textbooks, and drug databases to help oncologists choose the best treatment for their patients.
Forrester surveyed business and technology professionals and found that 58% of them are researching AI, but only 12% are using AI systems. This gap reflects growing interest in AI, but little actual use at this time. We expect enterprise interest in, and use of, AI to increase as software vendors roll out AI platforms and build AI capabilities into applications. Enterprises that plan to invest in AI expect to improve customer experiences, improve products and services, and disrupt their industry with new business models.
But the burning question is: how can your enterprise use AI today to crush it? To answer this question we first must bring clarity to the nebulous definition of AI.Let’s break it down further:
■ “Artificial” is the opposite of organic. Artificial simply means person-made versus occurring naturally in the universe. Computer scientists, engineers, and developers research, design, and create a combination of software, computers, and machine to manifest AI technology.
■ “Intelligence” is in the eye of the beholder. Philosophers will have job security for a very long time trying to define intelligence precisely. That’s because, intelligence is much tougher to define because we humans routinely assign intelligence to all matter of things including well-trained dachshunds, self-driving cars, and “intelligent” assistants such as Amazon Echo. Intelligence is relative. For AI purists, intelligence is more akin to human abilities. It means the ability to perceive its environment, take actions that satisfy a set of goals, and learn from both successes and failures. Intelligence among humans varies greatly and so too does it vary among AI systems.