AI and Automation has been a hot topic in the past decade, and right so, as it offers a lot of potential for businesses to accelerate growth, reduce costs and become more competitive. And there is this entire spectrum of just automating a business process with software, to making another process completely autonomous with more intelligent automation/AI.


SwissCognitive Guest Blogger: Ganesh Padmanabhan, Founder Stories in AI


But, despite the fact the AI is a powerful technology, you as a business leader should look at it as one of the several tools to drive digital transformation in your business. I’ve see several organizations take the AI-first, technology-first approach and while that accelerates the capability building and initial buy-in from stakeholders as something shiny and cool, a lot of the value realization from applying to business processes lag far behind.

And it’s often hard to see how to apply the right method to the right process. I had the pleasure of hosting an amazing set of panelists at the SwissCognitive Virtual this week, and we discussed the state of the industry along the spectrum of Automation to Autonomy for Business Process. (You can watch the recording here.)

Ongoing Panel Discussion at SwissCognitive, CognitiveVirtual about AI and Automation

During that discussion and drawing on my past experiences with global enterprises, I was beginning to see a framework evolving for building an Automation and AI strategy: It’s a three-part framework that can be useful to determine whether a process should just be automated and made more efficient, or whether it should be autonomized (intelligent automate end to end without human intervention).

  1. Quantitative inputs and outputs: A business process where the inputs are numbers and you are predicting riskiness through numeric or percent metric is usually a good candidate to fully automate. But processes like customer escalations handling where the inputs and outputs are usually not very structured, predictable or numeral is potentially a bad process to fully automate.
  2. Trust in Transaction: If the business process requires human to human connection to complete a transaction, where the human trust factor plays a huge role, then it’s potentially a bad candidate to fully automate. It’s usually a perfect candidate to augment the human with actionable insights and data, so they can focus on the human connection than remembering numbers and doing math.
  3. High Stakes Decisions vs. Low Stake decisions: This is an important criteria in determining if a process should be automated end to end. High stake decisions that has a huge cost of a mistake or legal or regulatory ramifications should be handled carefully as it will be hard to hold algorithms accountable. In decisions where accountability should be clear and until AI governance laws come into play, it will be important to have an employee lead the decisions augmented by AI. (Although autonomous driving seems to be an exception, but this accountability issue will be resolved before it becomes mainstream!)

What do you use as a framework for these decisions? What do you think about this framework. Please leave a comment, follow me at @_ganeshp (Twitter) or connect on LinkedIn.

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About the Author:

Ganesh Padmanabhan Keynote Speaker and Founder of Stories in AIGanesh is an accomplished business executive, entrepreneur and investor, with deep expertise in data and artificial intelligence (AI) related businesses. He is passionate about using technology to solve the biggest challenges for humankind and is a believer in the power of AI to augment human potential. He is an advocate for ethical use of data and AI, and using technology as a global equalizer to create opportunities for all.

He is currently the Founder of Stories in AI, an edu-tainment venture, and consults for several Fortune 500 organizations on Data & AI strategy and business models. He is an investor and advisor for several companies including Credo AI, OpsLab, Advanced Scanners, SuperWorld, Piggy Capital, Laureti Motor Corporation. He previous co-founded and scaled Molecula Corp, a data management company, and led growth and commercial scaling at CognitiveScale, Inc, an Enterprise AI company. Prior to that he spent a 15 year career spanning Dell Technologies and Intel Corp.