Why you can’t be cognitive without automation

By Katie Sotheran and Tuck Satterfield

Cognition is the process of collecting, remembering and analyzing inputs to derive insights and understanding. Humans have been doing it from our beginnings, and we’re good at it. So good, we’ve built tools to facilitate and automate cognitive tasks, helping us to think and learn more deeply or faster.

Now, cognitive technologies like artificial intelligence (AI) and machine learning (ML), along with the computers and devices running them, are part of everyday life — in our cars, phones, homes and offices. They underpin any number of daily digital transactions. According to Pew Research Center¹, 46 percent of Americans use voice assistants, such as Siri or Alexa, which use natural language processing and some ML to understand and respond to our requests. No big news here: humans are pretty comfortable interacting with available AI or cognitive technologies.

The bigger news is the shift in how these technologies are used, or plan to be used, in enterprises. AI is no longer about making people more productive. It’s about making whole workflows and systems more productive. As these systemic transformations become the norm, simple time savings will become a smaller part of the overall goal. Broader measures that reflect the health and quality of entire work processes and client journeys, like Net Promoter Score (NPS), will become the aim.

Achieving cognition at a scale to meet these new and further-reaching business goals will require business process automation. Automation is the mechanism for how AI and ML get translated into action. Automation is how you put AI to work.

Why you can automate without being cognitive, but you can’t be cognitive without automating

Automation has been around nearly as long as cognition, but when we talk about cognitive enterprises we’re talking about a more intelligent version of automation. Cognitive enterprises use software-based labor to execute entire workflows — rather than just certain tasks — which can fundamentally reshape how work gets done. These new intelligent workflows run digitally, in machine-time, with vast amounts of data flowing through them at a pace and volume humans simply can’t handle.

Think about all the data in your workflows and how it gets addressed by ML or AI to create decisions. The AI that handles the data needs automation capabilities — such as robotic process automation, workflow, or business rules — to execute those decisions. In other words, AI without automation is like a car with self-driving intelligence but no engine: you have the smarts but not the mechanism to execute on it.

Conversely, you can automate without being cognitive. Screen scraping or task automation can be done at scale with significant benefits. But these are bolt-ons that can provide some velocity and efficiency advantages; however, they don’t shift your business model to take advantage of the cognitive era or truly transform how work gets done. Keeping with the same smart car analogy, automation alone might give you a faster car but, without the AI, you still need to figure out how to get from point A to B and drive there yourself.

In sum, business continues to evolve. Cognitive and automation technologies are evolving even faster — in ways that are complementary. Gartner recently used the term “hyperautomation” to refer to “the combination of multiple machine learning (ML), packaged software, and automation tools to deliver work.”² Whether you call it hyperautomation or intelligent automation or something similar, it’s about making entire work processes and systems more productive. This requires a range of cognitive and automation technologies coming together to deliver both the intelligence and the power to put the intelligence into action.

If you’re interested in learning more about what it means to be a cognitive enterprise and how IBM is walking its talk:

  • Listen to the 19-minute interview with IBM Automation executive, Gene Chao, hosted by Daniel Newman, Principal Analyst at Futurum Research.
  • Read the seven keys to success.

Sources:

  1. “Nearly Half of Americans Use Digital Voice Assistants, Mostly on Their Smartphones,” Pew  Research Center. https://www.pewresearch.org/fact-tank/2017/12/12/nearly-half-of-americans-use-digital-voice-assistants-mostly-on-their-smartphones/ft_17-12-07_voiceasst_users/
  2. https://www.forbes.com/sites/peterhigh/2019/10/21/breaking-gartner-announces-top-10-strategic-technology-trends-for-2020/#743efda24074

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