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.
Two years ago, Forrester made the claim that mobile was the new face of social. With more than 3 billion users worldwide, messaging apps demonstrated one of the fastest-growing online behaviors and passed social networks. The reach of these apps is huge, which presents a strong relationship promise for marketers.
That is exactly what Forrester wants to find out - is there something behind the AI and Cognitive Computing hype? What my research directors ask, "Is there a there there?"
AI and Cognitive Computing have captured the imagination and interest of organization large and small but does anyone really know how to bring this new capability in and get value from it? Will AI and Cognitive really change businesses and consumer experiences? And the bigger question - WHEN will this happen?
It is time to roll-up the sleeves and look beyond conversations, vendor pitches and media coverage to really define what AI and Cognitive Computing mean for businesses, are businesses ready, where they will invest, and who they will turn to to build these innovated solutions, and what benefits will result. As such, Forrester launched its Global Artificial Intelligence Survey and is reaching out to you - executives, data scientists, data analysts, developers, architects and researchers - to put a finger on the pulse. We would appreciate you take a little time out of your day to tell us your point of view.
As a thank you, you will receive a complimentary summary report of the findings.
If you have a great story to share that provides a perspective on what AI and Cogntivive can do, what benefits is has provided your company, and can share you learnings and best practices, we are also recruiting for interviews.
Simply contact our rock star researcher, Elizabeth Cullen, to schedule 30 minutes. firstname.lastname@example.org
You can't turn anywhere without bumping into artificial intelligence, machine learning, or cognitive computing jumping out at you. Our cars brake for us, park for us, and some are even driving us. Our movie lists are filled with Ex Machina, Her, and Lucy. The news tells about the latest vendor and cool use of technology, minute by minute. Vendors are filling our voicemail and email with enticements. It's all so very cool!
But cool doesn't build a business. Results do.
Which brings me to the biggest barrier companies have in adopting artificial intelligence. Companies are asking the wrong questions:
What is artificial intelligence (or insert: machine learning or cognitive computing)?
In November, Forrester released its mobile predictions for 2016, highlighting how mobile will act as a catalyst for business transformation and explaining why the battle for mobile moments will redefine the vendor landscape.
Let’s now take a closer look at how mobile will impact marketing in 2016.
A year ago, Forrester argued that most brands would underinvest in mobile in 2015. This is likely to remain the case this year, since too many marketers still have a narrow view of mobile as a “sub-digital” medium and channel. This is good news for the 20% of marketers who told us they have the budget they need and for the 33% who said they know how to measure mobile ROI. In 2016, this growing minority of leading marketers will start to fully integrate mobile into their marketing strategies. These mature mobile marketers will measure the impact of mobile across channels, see a clear opportunity to differentiate their brands, and increase their investments in mobile initiatives. Here’s what else we expect to happen:
Integrating mobile into your marketing strategy will become a key differentiator. While most brands are trying to mobilize their ads, few are going the extra mile: serving their customers in their mobile moments by transforming the entire customer experience. Only those that do go that extra mile will differentiate their brands via mobile. Leaders will also start measuring the impact of mobile on offline channels and will end up allocating up to 20% of their marketing budgets to mobile.
You can't bring up semantics without someone inserting an apology for the geekiness of the discussion. If you're a data person like me, geek away! But for everyone else, it's a topic best left alone. Well, like every geek, the semantic geeks now have their day — and may just rule the data world.
It begins with a seemingly innocent set of questions:
"Is there a better way to master my data?"
"Is there a better way to understand the data I have?"
"Is there a better way to bring data and content together?"
"Is there a better way to personalize data and insight to be relevant?"
Semantics discussions today are born out of the data chaos that our traditional data management and governance capabilities are struggling under. They're born out of the fact that even with the best big data technology and analytics being adopted, business stakeholder satisfaction with analytics has decreased by 21% from 2014 to 2015, according to Forrester's Global Business Technographics® Data And Analytics Survey, 2015. Innovative data architects and vendors realize that semantics is the key to bringing context and meaning to our information so we can extract those much-needed business insights, at scale, and more importantly, personalized.
As companies get serious about digital transformation, we see investments shifting toward extensible software platforms used to build and manage a differentiated customer experience. My colleague John McCarthy has an excellent slide describing what's happening:
Before, tech management spent most of its time and budget managing a set of monolithic enterprise applications and databases. With an addressable market of a finite number of networked PCs, spending on the front end was largely an afterthought.
Today, applications must scale to millions, if not billions of connected devices while retaining a rich and seamless user experience. Infrastructure, in turn, must flex to meet these new specs. Since complete overhauls of the back end are a nonstarter for large enterprises with 30-plus years of investments in mainframes and legacy server systems, new investments gear toward the intermediary software platforms that connect digital touchpoints with enterprise applications and transaction systems.
At Forrester, we’ve been working to quantify some of the most viable software categories that exemplify this shift. A shortlist below:
· API management solutions: US CAGR 2015-2020: 22%.
· Public cloud platforms: Global CAGR 2015-2020: 30%. (Note: We have a forecast update in the works that segments the market into subcategories.)