Hollywood loves artificial intelligence (AI) because it can be used to create both wonder and fear in movie audiences. The type of AI most often depicted in movies is artificial general intelligence (AGI) — sentient machines that could decide humankind is either a nuisance or a threat (think HAL 9000 or Skynet). There are legitimate reasons sober thinkers like the late Stephen Hawking expressed concerns about AGI; but, as I’ll explain below, there are less concerning, more useful forms of AI being developed that are likely to reshape the world in the years ahead. In fact, Brian Burke, a Research Vice President at Gartner, predicts “80 percent of emerging technologies will have a foundation in AI by 2020.”
Types of artificial intelligence
For years, artificial intelligence was divided into two categories: weak and strong. Strong AI was defined as artificial general intelligence and weak AI was everything else. As the artificial intelligence field has matured, those definitions have become outdated. As I see it, there are currently three levels of AI being developed. They are:
- Weak AI: Wikipedia states: “Weak artificial intelligence (weak AI), also known as narrow AI, is artificial intelligence that is focused on one narrow task.” In other words, weak AI was developed to handle/manage a small and specific data set to answer a single question. Its perspective is singular, resulting in tunnel vision.
- Strong AI: As noted above, strong AI originally referred to Artificial General Intelligence (i.e., a machine with consciousness, sentience and mind), “with the ability to apply intelligence to any problem, rather than just one specific problem.” Today, however, there are cognitive systems that fall short of AGI but far surpass weak AI. These systems were developed to handle/manage large and varied data sets to answer a multitude of questions in a variety of categories. This is the category into which cognitive computing falls. Cognitive AI can deal with ambiguous situations whereas weak AI cannot.
- General AI: The AGI Society notes the ultimate goal of AGI researchers is to develop “thinking machines” (i.e., “general-purpose systems with intelligence comparable to that of the human mind”). The development of these potential thinking machines are what keep some scientists and many science fiction writers up at night.
In the near-term, weak and strong AI systems will dominate conversations and provide the most benefit to businesses. Cognitive systems should prove particularly useful since they can function in ambiguous situations — the kind of situations in which individuals and companies often find themselves. Burke describes the rapid penetration of AI systems into our lives as “the democratization of AI.”
AI in business
Former IBM executive Irving Wladawsky-Berger asserts an AI-driven economy is emerging. After reading a study published by PwC, he concludes, “Companies slow to adopt AI-based productivity improvements be warned: Artificial intelligence is the biggest commercial opportunity for companies, industries and nations over the next few decades.” He adds, “[According to the PwC study,] AI advances will increase global GDP by up to 14% between now and 2030, the equivalent of an additional $15.7 trillion contribution to the world’s economy.” Tarry Singh (@tarrysingh), CEO and Founder of deepkapha.ai, bluntly states, “In the AI economy, it’s a winner take-all game. It would be foolish to assume that you can hide under your pillow and not head out to be driving this change for your organization.”
If you are wondering what’s compelling these do-or-die warnings about AI, you are probably not alone. From Singh’s perspective, the urgency comes from the fact that digitization is creating new and disruptive business models that are altering the business landscape. He explains, “Most all companies and businesses are in the battle zone, and merely resting on new shiny platforms is not enough — push harder to move to disruptive AI platforms and adopt disruptive processes.” According to Wladawsky-Berger, pressure will come from consumers as well as competitors. He writes, “Network effects will further increase consumer demand. AI front-runners will gain an enormous competitive advantage through their ability to leverage this rich supply of customer data to shape product developments and business models, making it harder for slower moving competitors to catch up.”
Although AI adoption may be an imperative, AI is not a silver bullet. When making any investment, business leaders need to make a business case. Investing in AI is no different. What business challenges can AI help overcome? What pain points can it relieve? Because cognitive systems are so adaptable, finding a use case for AI shouldn’t be difficult. Nevertheless, business executives still need to make the case and figure out exactly what benefits they hope to achieve. And they need to be patient. J. P. Gownder (@jgownder), a Vice President and principal analyst at Forrester, asserts, “The road to realizing business value with these technologies is long and winding. … Particularly with AI, some organizations have set their sights on grand ambitions but underestimated the length of time, monetary commitments, and patience required to realize them.” Regardless of the fact the going may be slow, Gownder encourages companies to start investing in and experimenting with AI. “While AI and automation aren’t quick wins,” he writes, “companies that attack focused problems can drive business results today.” At Enterra Solutions®, we recommend a “crawl, walk, run” approach to AI projects so they can be tweaked along the way for maximum results before scaling.
Singh agrees with Gownder that business executives need patience, perseverance, and vision in order to leverage cognitive technologies successfully. “Businesses seem to be waking up to the call for applied AI but more needs to be done,” he explains. “It still needs to be seen who is doing this as a window dressing exercise and who is serious in this game. The steps to reach a mature level where organizations are fully equipped with the right talent, have adequate and secure infrastructure where they have access to developing custom-build algorithms, and an executive board that is not only aware but actively supportive of this transformation seems daunting.” Daunting, but not impossible. Wladawsky-Berger insists slow but steady steps will get you there. “Prioritize your response,” he writes. He suggests you can do that by asking the right questions. “How can different AI options help you deliver your business goals? What’s your readiness for change? Are you an early adopter, fast follower or follower? Are you trying to transform your business to keep up or to attempt to disrupt your sector?” The more completely you answer those kinds of questions the more comfortable you will get with AI and the sooner you will start seeing results you desire.
 Steve Rosenbush, “AI Driving Nearly All Emerging Technology, Gartner Says,” The Wall Street Journal, 15 October 2018.
 Irving Wladawsky-Berger, “The Economic Value of Artificial Intelligence,” The Wall Street Journal, 26 October 2018.
 Tarry Singh, “Artificial Intelligence In Enterprises – Businesses Are Waking Up,” Forbes, 22 October 2018.
 J. P. Gownder, “AI and automation aren’t quick wins — Invest anyway,” Information Management, 5 November 2018.