The topic of artificial intelligence has broken out of computer science labs and into boardrooms across industries, as recently noted in the Harvard Business Review: The buzz over AI has grown loud enough to penetrate the C-suites of organizations around the world, and for good reason. Investment in AI is growing and is increasingly coming from organizations outside the tech space. This news does not mean that Ultron will soon dominate the Fortune 500, but it does hint at the powerful new productivity paradigm that is reshaping enterprises.

What we broadly term "AI" (as opposed to traditional sequential computation) seeks to replicate the human mind's ability to “think” — to understand the underlying context of decision making based on multiple simultaneous inputs. Such technologies are already transforming a number of important business processes. Witness the prevalence of chatbots, facial recognition applications, self-healing networks, or virtual assistants such as Siri or Alexa that “learn” from interactions.

This paradigm has nothing to do with the self-aware cyberbeings popular in sci-fi.  Instead, it's deeply rooted in the quest to improve human capability. It is an “augmented intelligence” model defined by the positive-sum synergy between data-driven, technology-enabled analytics and human logic, business acumen, and intuitive contextual awareness.

Simply put, when you combine strong business strategies with powerful analytic tools and capabilities, the impact that teams can have on their businesses is far greater than relying solely on human effort or artificial intelligence alone. Big data and computational capacity growth have enabled incredible advancements in AI, but the deft intelligence of humans still plays an oversized role in translating those advancements into value creation.

AI myth-busting

Before exploring this augmentation concept further, it’s important to understand AI in the context of some pervasive misconceptions about its capabilities and applications to business.

What is augmented intelligence?

Augmented Intelligence deals in practicalities. It recognizes and applies the powerful combination of people and technology to define and solve complex business, scientific, and policy challenges.  And, the augmentation relationship between human and computer reaches both ways.

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The first case is well understood and widely utilized: AI, and analytics more broadly, improves the power of humans to make better, faster, minimally biased, and fact-based decisions (thus augmenting our intelligence). Quantitative models assign propensity scores based on historical data to future actions; algorithms give hedge funds an edge in picking, buying, and selling equities and options; bots automate repetitive or low-risk tasks.

The deficiencies of decision making when impacted by common cognitive biases that were initially discovered by the behavioral economists Amos Tversky and Daniel Kahneman illustrate the tremendous importance of augmenting human intuitive heuristics with data science. Essentially, most of us are susceptible to very common decision-making mistakes: We fail to take into account small or non-representative sample sizes; we base decisions on our most recent experiences rather than a longer period of behavior; we focus on factors that are readily available instead of exploring the full set of information; we anchor to numbers suggested to us versus of thinking rationally through the decision logic.

Data exploration and data science can insulate us from these mistakes by removing our biases. We just have to trust the data over our intuitive flaws.

Less appreciated is the way that human curiosity, intuition, and rigorous scientific methodology actually improve the efficiency and contextual relevance of analytics (humans augment analytic intelligence). This side of the discussion is arguably more interesting in industry, as it preserves and protects the role that people play in creating value for their organizations. Here are a few of the areas in which people substantially augment the power of analytics:

At least for now, we’re not expecting the robots to take over the world. So long as human and artificial intelligence are working in tandem, we’ll continue to make each other better at what we natively do best.

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David Rosen leads TIBCO’s Digital Transformation practice. In this role, David collaborates with customers in the midst of their digital journeys, mixing a strong knowledge of technology and a long history of consulting with C-level leaders of some of the world’s largest and most innovative companies. David drives much of TIBCO’s intellectual capital around digital transformation, combining TIBCO’s thought leadership with that of leading strategists and academics. Through his prior leadership of the TIBCO Reward strategy and analytics practice, David has provided value to all of TIBCO’s customers. David is a graduate of Dartmouth College and received his MBA from Stanford’s Graduate School of Business.